1

Personality Assessment from Text for MachineCommonsense Reasoning

Niloofar Hezarjaribi, Zhila Esna Ashari, James F. Frenzel, Hassan Ghasemzadeh, and Saied Hemati

Abstract—This article presents PerSense, a framework to estimate human personality traits based on expressed texts and to use themfor commonsense reasoning analysis. The personality assessment approaches include an aggregated Probability Density Functions(PDF), and Machine Learning (ML) models. Our goal is to demonstrate the feasibility of using machine learning algorithms onpersonality trait data to predict humans’ responses to open-ended commonsense questions. We assess the performance of thePerSense algorithms for personality assessment by conducting an experiment focused on Neuroticism, an important personality traitcrucial in mental health analysis and suicide prevention by collecting data from a diverse population with different Neuroticism scores.Our analysis shows that the algorithms achieve comparable results to the ground truth data. Specifically, the PDF approach achieves97% accuracy when the confidence factor, the logarithmic ratio of the first to the second guess probability, is greater than 3.Additionally, ML approach obtains its highest accuracy, 82.2%, with a multilayer Perceptron classifier. To assess the feasibility ofcommonsense reasoning analysis, we train ML algorithms to predict responses to commonsense questions. Our analysis of datacollected with 300 participants demonstrate that PerSense predicts answers to commonsense questions with 82.3% accuracy using aRandom Forest classifier.

Index Terms—Personality assessment, commonsense reasoning, machine learning, probability density function, personality trait.

F

1 INTRODUCTION

The demand for personalized services and adaptive tech-nologies such as driver-less cars are projected to growrapidly and will soon be integrated into our daily life. Thecommunication of these technologies with human-beingsneeds to be proper and well-trained in order to serve,explain the facts, and understand different mindsets better.Automating the process of understanding individuals’ pri-orities and their perception of the world can potentially beused to address specific needs of individuals and to estab-lish efficient interactions among human and technologies.Human commonsense reasoning involves human factorsand personality traits in the process of judgment and deci-sion making, and it goes beyond the conventional logical de-ductive or experimental inductive reasoning. These humanfactors include perceptions, desires, herd mentality, men-tal stability, prejudice or other biases, behavior traits, andpersonal/ethical/religious values. Therefore, commonsensereasoning is difficult to formulate for smart technologies.As an example, the complexity of predicting stock marketfluctuations is due to human factor or market psychology,and is not well understood by human-beings [1]. Similarly,the rise and fall of political figures in a democratic society isnot necessarily merit-based and not even deterministic andtime-invariant [2]. In marketing, other than functionalityand appearance of a product, consumers’ evolving taste

This work was supported in part by a grant awarded by Samsung GlobalResearch Outreach (GRO) program in 2016. Niloofar Hezarjaribi, Zhila EsnaAshari and Hassan Ghasemzadeh are with the School of Electrical En-gineering and Computer Science, Washington State University, Pullman,WA 99164-2752, USA. Emails {n.hezarjaribi, z.esnaashariesfahan, has-san.ghasemzadeh}@wsu.edu. James F. Frenzel and Saied Hemati are with theDepartment of Electrical and Computer Engineering, University of Idaho,Moscow, ID, 83844-1023, USA. Emails: {jfrenzel, shemati}@uidaho.edu.

contributes to a brand popularity and its success or failure.Understanding the limitations and ethical consequences ofcommonsense reasoning is paramount and requires devel-opment of machine commonsense reasoning.

In philosophy, the distinction between something rea-sonable and not reasonable is highly challenging to formu-late, especially when a person should choose between twomorally questionable acts [3]. The trolley problem, whichis a well-known ethical thought experiment discussed byPhilippa Foot, is an example of a highly challenging task.In the trolley problem, it is presumed justifiable for a driverof a runaway trolley that is moving towards five railroadworkers to divert the trolley to a branch where only oneworker is on the track. 86% of participants in the surveybelieved saving five lives is more important than saving asingle life [4]. However, a surgeon may not kill a patientor even let the patient die to distribute the patient’s vitalorgans to five other patients that would otherwise die. Themath seems identical, sacrificing one to save five, but onedecision seems reasonable and justifiable to many peopleand the second case is not. The hypothetical trolley problemcan be extended to realistic scenarios with driver-less cars[5]. A driver-less car should be equipped with commonsensereasoning for simply maximizing the gain and minimizingthe loss in its decision making process.

The complexity of commonsense reasoning problem canbe further explained by IBM Watson, the question answer-ing computing system that defeated Jeopardy championsin 2011. Watson had special tools for analyzing naturallanguage and had access to 200 million pages of informationincluding the Wikipedia encyclopedia with no Internet ac-cess. Jeopardy questions were facts not human perceptions.However, in response to the question that in which US city,the largest airport was named for a World War II hero and its

arX

iv:2

004.

0927

5v1

[cs

.CY

] 1

5 A

pr 2

020

2

second largest airport was named for a World War II battle?Watson answered: “What is Toronto?”. The answer was avery uneducated guess as Toronto is not even located inUS! The supercomputer had no human-like commonsensewhen answered this question. Obviously, not all differentscenarios can be programmed or thought through aheadof time. If we ask the same question from a large groupof children, the likelihood of hearing “Toronto” as the an-swer is projected to be extremely low, even though theyprobably will not know the answer. Although IBM Watsoneasily defeated the Jeopardy champions, the real problemarises when IBM Watson is asked about human perceptionsregarding issues that are not necessarily factual or for whichthere exists multiple correct answers for one question. As anexample, it is trivial to list the name of all animals whichwere mentioned in the Bible. However, it is challenging forWatson to guess the most common answer of general publicin US to this question.

In this paper, we hypothesize that “commonsense rea-soning” is mostly grounded in human personality traits. Todate, human personality traits have remained a virtually un-explored sources of information for understanding humanbeings’ priorities and their perception of the world. Thereare numerous standardized questionnaires that have beendeveloped for personality trait analysis [6]–[8]. However,this traditional approach is based on the active participationof subjects in the conducted study. The validity of theseapproaches also depends on the subjects’ honestly anddedication to correctly answer to the questions. Recently,a passive form of personality profiling is gaining interestin which the subjects’ natural behavior is examined, ratherthan the choices that they intentionally make in fillingquestionnaires. The passive approach is projected to moreaccurately reflect the truth if the subjects’ behavior over anextended period of time is available. The passive approachcan also exploit vast information that is readily available onsocial media, from product reviews on Amazon to Twitterand Facebook posts [9], [10]. The passive form of personalityprofiling is projected to shed more light on human mindsetby exploiting the massive information that is readily avail-able on the Internet.

In an entirely new direction, we propose PerSense, anovel framework for personality assessment and common-sense reasoning. The overall approach to depict the de-scribed hypothesis is shown in Figure 1. The relationshipbetween the personality and language is presumed to bepresent in two different directions and personality trait isalso hypothesized to be related with commonsense reason-ing. Therefore, our innovative work in this project aims to:

1) develop computationally-simple algorithms to es-timate human personality traits based on user’sdigital footprint on social media,

2) exploit the connection between personality trait andcommonsense decision making, and

3) demonstrate the feasibility of predicting human be-ings’ response to open-ended questions that have nounique factual answer.

Therefore, PerSense is a framework for commonsense rea-soning based on personality traits. It first employs texts fromdifferent sources such as Amazon reviews for analyzing

individuals’ personality. We designed two low complexityapproaches for estimating personality traits based on textdata in PerSense. In the absence of a large scale surveybased on standard personality traits questionnaires, thepersonality scores generated by the IBM Watson tool areused for ground truth labeling. These scores, which are realnumbers between 0 and 1, are then utilized for statisticalanalysis and machine learning model training focused onpersonality analysis. Finally, we exploit human personalitytraits and their distributions in a society to develop machinecommonsense reasoning. To this end, we use a machinelearning based approach where we train the algorithmsusing data collected from hundreds of subjects throughAmazon Mechanical Turk.

2 RELATED WORK

In this section, we discuss related research on personalityassessment via language and text. We also discuss previousresearch on commonsense knowledge and reasoning.

2.1 Personality Assessment

Personality has been found to significantly correlate witha number of real-world behaviors. It correlates with musictaste: popular music tends to be significantly liked by ex-troverts, while people with a tendency to be less open toexperience tend to prefer religious music and dislike rockmusic [32]. Personality also impacts the formation of socialrelations [36]: friends tend to be, to a very similar extent,open to experience and extrovert [35].

Human personality traits, thoughts, emotions, and judg-ments are amongst the important topics in psychology [11],[12]. Since the most universal way of expressing humanfeelings and thoughts is via language, it is reasonable toassume personality trait and language are related [13]–[15].The lexical hypothesis, which is one of the well-establishedpropositions in psychology, upholds this assumption [8].The lexical hypothesis states that any language provides anunbiased source of different personality types. Thus, it pos-tulates the feasibility of extracting out personality types andpsychometric features by examining the linguistic represen-tation of people [16], [17]. The measurement of psychometricfeatures, which highlight individual differences [18], mustbe reliable in the sense that the measurement method andtools provide stable results across different circumstances.The measurement must also be valid in the sense that theresults remain correct for various purposes [19].

The Big Five model or the five factor model (FFM) [20],is a scheme of personality traits classification [21]–[23]. Thismodel utilizes the evaluation of individuals’ personalitytraits based on their ranking on five bipolar factors calledOCEAN (Openness, Contentiousness, Extroversion, Agree-ableness, and Neuroticism) [24], which is traditionally doneusing standard questionnaires. The validity of the Big Fivemodel depends on the validity of the lexical hypothesis[25], [26]. Accordingly, the validity of the lexical hypotheses,depends on its initial claim that personality is accuratelyrepresented within the structure of the language. There areinteresting applications for the Big Five model. For example,It has been shown that extroverted individuals have more

3

LanguagePersonality

TriatCommonsense

Reasoning

Fig. 1: Utilizing personality traits for commonsense reasoning from language.

Facebook friends [27]. The users with high Neuroticismscores, on the contrary, post more on Facebook wall [28].Another application of Big Five model is in prediction of jobperformance [29].

There are a few technical reports and papers on ex-tracting out personality from lexicon which is called theFactor Analysis. There are different methods for factor anal-ysis. The first and conventional methods where based onthe personality traits description questionnaire [30]–[32]. Itextracted out relevant information about personality fromdescriptive adjectives and different questions and phrasesrelated to personality. The automatic recognition of person-ality traits was first presented in [33]. Recent developmentsin Artificial Intelligence (AI) explore new methods for au-tomatic detection of linguistic features in text. For example,Roderick Hart developed the DICTION program in 2001,which counts the words in a text related to a particulartheme [34]. The DICTION was a word count program toreveal the verbal tone of political statements. The five mastervariables (activity, optimism, certainty, realism, and com-monality) that provide a robust understanding of a text wereused as a basic characterization of a text. A well-known toolfor linguistic analysis is Linguistic Inquiry and Word Count(LIWC) [16], [35], [36], which is an a priori word-categoryapproach. The LIWC method relies on an internal defaultdictionary (a priori word-category) that defines which wordsshould be counted in the target text files. For example,the word “cried” is part of five word categories: sadness,negative emotion, overall effect, verb, and past tense verb.

There are many available user text datasets such as socialmedia, on-line reviews, and essays which can be utilizedin inferring personality and building models for predictingan individual’s behavior in society [37]–[39]. In [40], anapproach was proposed for capturing physical proximity,location, and movement. Later, these data were utilized formodeling human behavior. Phone and email logs can pro-vide some patterns about human communication. A studywas provided by Onnela et al., in which the communicationpatterns of millions of mobile phone users were utilized forobserving the coupling between the strength of interactionsand the structure of the network [41]. In a work proposedby Eagle et al., physical proximities were collected over timefor inferring cognitive relationships, such as friendship [42].

The Internet is the main source for understanding thebehaviors and connections of the people [43], [44]. Personal-ity has impacts on people’s online interaction. In a research

study, it is claimed that user’s personality can be predictedusing Facebook data, accurately [45]. They utilized linearregression for predicting user’s personalities. The inputfeatures included word counts, long words, words abouttime, health, etc. Mean absolute error for this predictionwas 11%. In another study, the impact of users’ personalityon Twitter activities was investigated [46]. In this research,the relationship between big five personality traits and dif-ferent types of Twitter users was studied. Moreover, user’spersonality traits were predicted based on the number offollowings, followers, and times the user has been listed onother’s reading list. For predicting each personality trait,they utilized a 10-fold cross validation with 10 iterationsusing M5′ rules. The maximum root-mean-square error(RMSE) is 0.88. In this work they utilized basic networkproperties in user’ Twitter account. In our research, wecollected data from different sources and text analysis wasperformed. Moreover, we utilized two different approaches,statistical and probabilistic, for predicting personality traits.In another research, they proposed that Instagram picturefeatures can be correlated with personality [47]. They triedto predict personalities based on the way the pictures weretaken and filters applied to them. Features were extractedfrom 22,398 pictures and mean values were calculated foreach feature to create a measurement of central tendency.Later, a correlation matrix was created based on the centraltendencies. Their findings suggested a relationship betweenusers’ personality traits and their posted pictures.

2.2 Commonsense Reasoning

Knowledge representation and reasoning are importantproblems in artificial intelligence. There are different rulesand methods for knowledge representation [48]. In orderfor a computer to act intelligently, it should have a wideknowledge representation of the world. Designing such acomputer, requires information about the type of knowledgeand the ways of obtaining it. The reasoning problem isa philosophical problem that can be solved clearly usingintelligent systems. In reasoning programs, interaction withthe world are through inputs and outputs that can berepresented in different forms. One of these representationsutilizes a set of sentences in the form of logical language.

Commonsense knowledge is the most general kind ofknowledge that belongs to all people [49]. There exist sev-eral properties such as defeasibility and context sensitivity

4

[50], which distinguish logic reasoning from human com-monsense reasoning. Therefore, the use of natural languageis considered to be the central component for commonsenseknowledge representation [51]. In order for a system toprocess the natural language similar to human beings, itmust find the relations and information about the wordsand their meanings. In a study by Liu et al. a systemcalled ConceptNet is proposed wherein natural language isexplored as a tool for commonsense representation [49]. Ina work performed by George A. Miller, a framework calledWordNet is proposed which benefits from the relationshipbetween words [52]. In another work by Singh et al. a systemcalled Open Mind is designed that obtains commonsenseknowledge from people over the web [53]. A calender withcommonsense called SensiCal is presented by Mueller thatgenerates warnings for avoiding errors [54]. For example,SensiCal sends a message to the user not to take a friendthat is a vegetarian to a steakhouse.

3 PERSENSE FRAMEWORK DESIGN

PerSense aims to perform commonsense reasoning based onan individual’s personality. An overview of the PerSenseframework for algorithm design is shown in Figure 2. De-signing the PerSense framework takes three phases including1) training data construction, 2) personality assessment,and 3) commonsense reasoning. In order to construct thetraining data, text data from different sources are gathered,natural language processing is applied on the data to extractimportant information from the text, and the personalitytraits associated with each text sample are scored using theIBM Watson Personality Insights engine. The text data, theinformation extracted using the NLP algorithms, and thescores achieved through the IBM Watson engine are thenstored in a database. After gathering the labeled trainingdata, PerSense estimates personality traits for each text sam-ple. Two models are proposed for estimating personalityscores including 1) a probabilistic approach based on Aggre-gated Probability Density Functions (PDF); and 2) an ML-based approach that utilizes machine learning algorithms ina supervised fashion to learn a model that classifies text datainto personality classes. After performing personality as-sessment, PerSense performs machine commonsense reason-ing based on each individual’s different personality traits.PerSense uses a supervised learning based methodology forcommonsense reasoning. In what follows the computationalalgorithms utilized in PerSense framework are explained inmore detail.

3.1 Probabilistic-Based Personality Assessment

Let us assume N text samples have been processed and forthe score s, a real number between 0 and 1 ( 0 ≤ s ≤ 1),is associated with a given personality trait T , such as Neu-roticism. We assume g(s1, s2) is the function that shows thenumber of text samples, among N processed text samples,for which the personality trait score is between s1 and s2,where s1 ≤ s2 (g(0, 1) = N ). The score interval [0, 1]is divided equally into n sub-intervals of [s0, s1], [s1, s2],· · · , [sn−1, sn], where s0 = 0 and sn = 1. In a uniformdistribution of samples, g(si, si+1) = N

n , where i is any

Natural Language Processing

IBM Watson Personality Traits

Labeled Training Data

Machine Learning Model Training

Probability Density Function Model

Text Data Acquisition

Personality Assessment

Trai

nin

g D

ata

Co

nst

ruct

ion

Pe

rso

nal

ity

An

alys

is

Machine Learning Model Training

Commonsense Reasoning Co

mm

on

sen

se

An

alys

is

Fig. 2: An overview of the PerSense framework.

positive integer number between 0 and n − 1. In practice,it is unlikely to have a uniform distribution of samples asthe chance of observing people with very high or very lowscores is less than observing people with moderate scores.In any statistical analysis, increasing the number of sampleswill increase the accuracy of the results. Consequently, thenon-uniform distribution of samples will adversely impactthe accuracy of the results. However, in a sufficiently largesurvey there will be a sufficient number of samples in anyinterval.

Any collected data in the k-th sub-interval, where 1 ≤k ≤ n, will be divided by the number of text samples inthat interval (g(k−1

n , kn )) to generate a normalized metric.This metric indicates the likelihood of using a specific word(in this study an adjective) by authors whose personalitytrait score is in the k-th interval for the personality trait T .To achieve this objective, we create a histogram for eachadjective with regard to a particular personality trait overpersonality trait intervals [s0, s1], [s1, s2], · · · , [sn−1, sn]. Forexample, if a word w occurred x times in a text sample andthe personality score for that text sample for a trait T is sy ,which is in the k-th interval, i.e., k−1

n ≤ sy ≤kn , a count of x

will be added to the number of times w has been observedin the text samples authored by subjects whose scores arelocated in the k-th interval. By processing a large number oftext samples, i.e., N,n→∞ and normalizing the generatedhistogram by its integral from 0 to 1 to have an area equal to1, the histogram will be converted to a probability densityfunction, fw,T (s). The probability that an author that uses win her writing has a specific personality trait score betweenα and β is calculated by

∫ βα fw,T (s)ds.

It is equally important in probabilistic-based and ML-based approaches to find words that convey distinctive

5

information regarding personality traits, i.e., their proba-bility density function is not uniform, i.e., fw,T (s) 6= 1, atleast for one personality trait. The words with the smallestvariance in their probability density functions are moreselective and convey more information. By developing thedatabase and creating the probability density functions forinformative words, it becomes possible to analyze new testsamples without relying on IBM Watson. Any new textsample is processed and its informative words are extracted.The related probability density functions for informativewords, which have already been developed, are combined tocreate an aggregated probability density function (PDF) foreach personality trait. The aggregated probability densityfunctions are then used to estimate the personality trait ofthe author.

Let us assume, w1, w2, · · · , wm are m informative wordsin a sample text, where m is a positive integer and thenumber of distinct informative words can be less than m,which means some informative words can be used morethan one time in a sample text. The corresponding probabil-ity density functions are fw1,T (s), fw2,T (s), · · · , fwm,T (s),respectively. The aggregated probability density functionfor the personality trait T of the text sample, ΦT (s), iscalculated by the following equation.

ΦT (s) =

m∏i=1

fwi,T (s)∫ 1x=0

m∏i=1

fwi,T (x)dx(1)

Here, we made a simplifying assumption that utilizingan informative word is a completely independent eventand conveys no information regarding any other infor-mative words in the text sample, not even regarding thesame informative word if it has been used multiple times.In other words, the fact that the subject author used w1

in the first sentence of the text sample, has no infor-mation about using wi in the text sample, where i isan integer number between 1 and m. As an example, iffw1,T (s), fw2,T (s), · · · , fwm,T (s) = 1 and convey no in-formation, ΦT (s) will also be equal to 1 and will conveyno information regarding the personality trait T . The sametechnique can be expanded to additional informative words(verbs, pronouns, etc.) as well as additional text features,such as the use of passive and active voice, capitaliza-tion, punctuation, spelling or grammatical errors, sentencelength, and word position.

3.2 Machine Learning for Personality AssessmentAlternatively, we developed a second approach for person-ality assessment based on supervised learning methodolo-gies. There are a large number of methods for classificationand regression in supervised learning research. Examplesinclude tree-based, naive-based, support vector machines,etc. The classifiers and regressors do the predictions basedon the input features to the system and their specific pat-terns. Supervised learning methodologies are useful tech-niques when a straightforward mathematical model cannotbe extracted or building a statistical model is expensive.These techniques infer decisions based on a set of trainingdata. The training data are divided into two sets:

1) X is a finite set of input features,2) Y is a finite set of output labels associated with each

input feature instance, and3) F (X) is a function that maps input features to

output labels, i.e., XF (X)→ Y

In this work, the input features are a set of adjectivesutilized across the text samples and their frequencies in eachtext sample. The output labels are the personality scoresassociated with each text sample. F (xi) is a classificationalgorithm that generates an output label yi as the predictedscore/label for samplei. The purpose of training is to findthe mapping function from the input features to the outputlabels; therefore, if we feed a set of input features into ourdecision making function, it will provide a prediction of theoutput label. The schematic of the machine learning methodis shown in Figure 3.

3.3 Machine Learning for Commonsense ReasoningWe utilized a supervised learning based methodology forcommonsense reasoning. Several classification and regres-sion algorithms are utilized for modeling the problem.As mentioned previously, a supervised learning methodderives the decisions based on the inputs that are givento the model. These inputs are represented as features inthe context of machine learning modeling. For the pur-pose of commonsense reasoning, we designed a way ofhuman-computer interaction wherein the input features areresponses to a standard FFM personality trait questionnaire[55]. This online FFM questionnaire was developed from theInternational Personality Item Pool (IPIP). A revised versionof this was utilized for this work which was created withacceptable reliability and factor uni-vocal scales. FFM modelwas proved to be acceptable for use in internet-mediatedresearch via different experiments. We perform classificationfor each individual commonsense question. The predefinedresponses to commonsense questions are output labels forthat question. The schematic of the supervised learningmethod utilized for commonsense reasoning is shown inFigure 4.

4 EXPERIMENTAL SETUP

This section describes the steps that are taken for evaluatingPerSense. In order to prevent propagation of errors from per-sonality assessment algorithms to commonsense reasoningtechniques, we evaluated these two algorithmic modulesindividually. The final dataset for personality analysis usingML contains 16, 934 data points, 345 input features, and 8class labels for each personality trait. The dataset for com-monsense reasoning consists of 300 data points, 50 inputfeatures, and depending on each commonsense questionthere are different numbers of input features ranging from 2to 7.

In this work, for evaluating the proposed framework,two sets of experiments where designed. First experimentis designed for evaluating personality assessment. For thispurpose IBM Watson’s text-based personality trait estima-tion tool was utilized for ground truth labeling. The ac-curacy of the proposed method was thus limited by IBMWatsons accuracy. However, recruiting tens of thousands of

6

Data Sources

Data sanitizer

Data Fusion

Am

azo

n r

evie

ws

Ess

ays

Blo

gs

POS tagging Feature

Extraction

Labels

Feature vector

Predictions Features

New data

. . .

. . .

. . .

Supervised Learning

Model

Input Data

Data sanitizer

Data Fusion

Per

son

alit

y t

rait

s

Feature Extraction

Labels

Feature vector

Predictions Features

New Question

. . .

. . .

Supervised Learning

Model

Fig. 3: The pipeline of the personality assessment framework using supervised learning methodology.Data Sources

Data sanitizer

Data Fusion

Am

azo

n r

evie

ws

Ess

ays

Blo

gs

POS tagging Feature

Extraction

Labels

Feature vector

Predictions Features

New data

. . .

. . .

. . .

Supervised Learning

Model

Input Data

Data sanitizer

Data Fusion

Per

son

alit

y t

rait

s

Feature Extraction

Labels

Feature vector

Predictions Features

New Question

. . .

. . .

Supervised Learning

Model

Fig. 4: The pipeline of the commonsense reasoning framework.

volunteers to provide text samples and take standard per-sonality tests was not a feasible option for this preliminarywork. In the second part, standard personality tests resultswere utilized for commonsense reasoning, and the groundtruth is generated by questionnaires filled through AmazonMechanical Turk.

In what follows, the steps for data collection and ex-perimental setup are explained in detail. We will discussthe obtained results based on these experiments in the nextsection.

4.1 Experiments for Personality Assessment

For evaluating the performance of the personality assess-ment algorithms, we construct a dataset of text samples toevaluate both approaches (i.e., PDF and ML) by utilizingexisting texts from Amazon movie reviews [37], stream-of-consciousness essays [56], and blog posts from sites hostedby WordPress. The mined text sample dataset contained17, 128 data points. These text samples are given a score foreach personality by the IBM Watson Personality Insights,a commercialized text-based personality assessment toolrunning on a big supercomputer and providing other fee-based services. In this work we utilized the IBM Watsonservice for assessing the personality characteristics based ona user’s digital footprint and communications [57]. This toolhas limitations on the text inputs. Moreover, some of theadjectives are not descriptive due to having a low frequencyin the samples. Therefore, the text samples that don’t meetthe following criteria are filtered out:

1) text samples should be in English;

2) the number of words in each sample should begreater than 600; and

3) each adjective frequency should be greater than 25throughout all the samples.

The mined text sample dataset contained 16, 834 data pointsafter data filtering.

The scores given by IBM Watson are real values in therange of 0 to 1, with 0 being the lowest in that personalitytrait. These scores are used as ground-truth labeling for test-ing our PDF and ML approaches for text-based personalitytrait assessment. The collected data were stored in a localdatabase that consists of two tables. The first table has thelist of all the adjectives, their frequencies, and a vector ofscores associated with the samples that they were found in.The second table has all the text samples, a vector of ad-jectives and their frequencies in that sample, and the scoresgiven by IBM Watson. Input features for training models forpersonality assessment are frequencies associated with eachadjective. The output labels are the scores given to each textsample by IBM Watson.

To build the training data in PerSense, the aggregateddata are passed through Natural Language Processing(NLP) tools for extracting important data and their frequen-cies. For this purpose, the Natural Language Toolkit (NLTK)[58] is utilized, which uses large bodies of linguistic data,or corpus. The NLTK is written in Python and distributedunder the GPL open source license. We utilized existing cor-pora in the NLTK which are large text bodies for aggregatinga list of existing adjectives, such as Gutenberg, web and chattext, Brown, and Reuters. A total of 26, 414 adjectives werefound in the corpora. Utilizing this tool, we tagged the sen-

7

TABLE 1: The distribution of the data for each class label.

Labels O C E A N0-0.1 0.3% 35% 31% 60% 6%

0.1-0.2 0.6% 26% 20% 17% 6%0.2-0.3 0.8% 15% 15% 8% 11%0.3-0.4 1% 9% 11% 4% 17%0.4-0.5 1% 5% 8% 3% 18%0.5-0.6 1% 4% 5% 2% 16%0.6-0.7 1% 2% 4% 2% 11%0.7-0.8 2% 2% 3% 1.5% 7%0.8-0.9 2% 1% 2% 1% 4%0.9-1 90% 1% 1% 0.4% 3%

tences in all of the text samples and extracted the adjectivesand their associated frequencies in the text samples. For thepurpose of ground-truth labeling, the dataset is analyzedby IBM Watson for assigning personality scores to each textsample. The data obtained from these two sources, NLP andIBM Watson, are then stored in a database. In what follows,the dataset for each personality assessment approach andstatistics of the data are explained in more detail.

4.1.1 Distribution of Scores

To obtain ground-truth labeling, the dataset was fed tothe IBM Watson and scores were assigned to each textsample in the dataset. Out of different personality traits,OCEAN traits were extracted from the results. The resultedscores by IBM Watson were divided into 10 class labels inorder to see the distribution of data. As shown in Table 2,Openness score for 90% of the data was above 0.9, whilethe remaining 10% of the data had a score between 0 and0.9. Although all personality traits are equally important,the most uniform distribution of the mined samples wasobserved for Neuroticism. This personality trait is importantin mental health care and suicide prevention. Therefore, ourtext-based analysis was focused on this personality trait.

4.1.2 Dataset Preparation for PDF Analysis

While 26, 414 adjectives were obtained from the NLTK cor-pora, only 17, 771 were used in our text samples. At first, wedivided the personality scores into 10 subsets (n = 10). TheIBM Watson scores that were below 0.1 and higher than 0.9seemed dubious and were typically from short text samples.Therefore, we limit the analysis to scores between 0.1 (10%)and 0.9 (90%). The labels for each subset are shown inTable 2.

The frequency of all the adjectives were computed sepa-rately for each sub-interval. The adjectives with frequenciesless than 300 over all the text samples were counted asunreliable data as there were zeros in the sub-intervals,resulting in Φ(s) = 0 in the related sub-interval based on (1).The final number of adjectives used in this study was 868.Figure 5 shows examples of PDF vectors for two adjectives:Major and Happy.

After forming probability density function vectors foreach adjective, we calculated the aggregated probabilitydensity function (PDF) for each text sample. The text sam-ples with less than 1000 and more than 6000 words wereeliminated. Text samples with less than 1000 words were tooshort to have reliable results using IBM Watson. A few text

(a)

(b)

Fig. 5: The PDF values for two adjectives and the correspondingNeuroticism score: (a) Major — (0.4-0.5), and (b) Happy — (0.2-0.3).

samples larger than 6000 words, up to one million words,were mined. These samples turned out not to be a usualtext sample written by a subject reviewing a product orcommenting about something and thus were discarded.

Furthermore, we noted that we have more samples at thecenter and fewer samples at the corners, so the g functionwas not uniform. In order to calculate each text sample’sPDF vector, we extract the adjectives in each text sampleand multiply their vectors. Finally, the PDF values are scaledto have an integral equal to one. Then the peak of eachvector shows the personality score for that text sample.An example of a PDF output for Neuroticism is shown inFigure 6. This figure shows that the Neuroticism score forthis specific text sample is in the range of 0.2 to 0.3.

4.1.3 Dataset Preparation for ML AnalysisThe initial set of input features are the total number of adjec-tives that were identified through the NLTK. After taggingthe adjectives throughout all the text samples, the numberof adjectives came down to 3, 834. Thereafter, adjectives thathad a frequency of less than 10% of the total number ofoccurrences of all the adjectives throughout the data pointswere removed from the feature set. The number of adjectivesafter data filtering was 345. Other than filtering the feature

8

Fig. 6: Neuroticism PDF for a specific text sample.

TABLE 2: The division of the personality scores and labels foreach section.

Personality Score Range Label0.1-0.2 0.150.2-0.3 0.250.3-0.4 0.350.4-0.5 0.450.5-0.6 0.550.6-0.7 0.650.7-0.8 0.750.8-0.9 0.85

set, the data points were filtered as well. The number of datapoints before data filtering was 17, 127. The frequencies ofadjectives in each data point were enumerated, and datapoints with fewer than 5.5% of the total number of featureswere removed from the dataset.

The final dataset contains 16, 934 data points and 345input features. Each xij in the feature set is the number ofadjective i in data point j.

4.1.4 Regression and Classification ModelsClassification is a form of predictive modeling and it is fo-cused on learning a mapping function from input features tooutput labels. The classification model is used for predictinga discrete class label output. The regression model, on theother hand, is used for predicting a continuous set of classlabels.

The main motivation of this analysis is to quantify thecorrelation between adjective usage in each text sampleand the personality scores associated with that sample. Thecorrelation of the adjectives and the personality scores isestimated using regression models. As mentioned above,the dataset contains 345 features after the process of featureextraction and feature selection. The regression models uti-lized in this work are Support Vector Regression (SVR) withradial basis function (rbf) kernel [59], Linear Regression [60],and Random Forest Regression [61].

Moreover, we tried to predict the output labels usingclassifiers as well. Six popular supervised learning classifierswere utilized for modeling the personality predictor, namelySupport Vector Machine (rbf, degree 2 polynomial, andlinear kernels) [62], Decision Tree [63], K-nearest neighbors[64], Perceptron [65], Multi-layer Perceptron [66], and Ran-dom Forest classifier [67].

4.2 Experiments for Commonsense Reasoning

In this section, the setup of the experiments for common-sense reasoning based on the users’ personality traits areelaborated. For the purpose of training a prediction model,data were collected using Amazon Mechanical Turk andFacebook. A test was designed for assuring the consis-tency of answers to survey questions. After passing theconsistency test, we obtained 300 survey response and eachresponse was considered as a data point. In what followsthe data analysis steps are explained in detail.

4.2.1 Commonsense Dataset

For building the dataset for commonsense reasoning, wedesigned a survey with two parts. The first section of thesurvey carries 50 personality trait questions. This question-naire contains standardized five factor personality test. Theresponses are in the form of a Likert scale which is com-monly involved in research studies that employs question-naires or surveys: very inaccurate (1), moderately inaccurate(2), neither inaccurate nor accurate (3), moderately accurate(4), very accurate (5) [68]. The first set of questionnaire(personality questions) are input features for training com-monsense reasoning models. In this article, input featuresare a set of scores given to each personality question by theuser. There are 550 possible outputs for standard personalityquestionnaire. Questions are expected to be independentand orthogonal, resembling eigenvectors in a vector space,each targeting a unique dimension of human personality.The FFM scores, historically were designed to be concise andhandy for general statements not necessarily an adequatetool in big-data era and do not convey equal informationfor common sense analysis.

The second section of the survey contains 67 questions,75% from the game show Family Feud [69], and 35% de-veloped in our lab. The developed questions are based onSamsung’s interest, popular political questions, and health-care problems. A model is trained for each commonsensequestion; therefore, the output labels are the answers to eachquestion (e.g. Question: “If money is not an issue, you willbuy a driver-less car ...”, Answers: 1. Right away, 2. Onlywhen 25% of cars are driver-less 3. Only when you have noother option 4. Only when 50% of cars are driver-less).

The distribution of class labels was calculated for eachquestion. If the class labels were not well distributed fora question, we tried to merge related labels and make theresults uniformly distributed. The labels for each questionwere merged based on the relevance of each label to an-other on for a particular question. This relevance of eachlabel to another one was determined by perception of theexperimenter of those labels to be positive or negative. Forinstance in the question about free health-care (What isyour opinion about creating united health care insuranceby slightly increasing the sales/income tax?), two answersagreed and two answers disagreed with this. Therefore,the experimenter merged two negative answers and twopositive answers.

The label distributions of a subset of commonsensequestions are shown in Table 3. This table shows examplesof commonsense questions and the distribution of classlabels for each question. As an example, row two in Table 3

9

(“President Trump’s travel ban is”), shows a question withfour possible choices (1. “Great and improves the safetyof legal and patriotic US citizens”, 2. “Not good, becauseit is discriminatory”, 3.“Good, but should include morecountries”, 4. “Not good, since not all Muslim people in thebanned countries should be punished because of the Sept.11 attack”). The distribution of responses to this questionfor each choice was 25%, 17%, 10%, and 48%, for each of thefour above-mentioned questions. Based on the distributionand relevance of these options, responses for choices 1 and3 were combined and responses selecting choices 2 and 4were combined.

4.2.2 Prediction ModelEight popular supervised learning classifiers were utilizedfor developing a machine commonsense reasoning modelthrough human-computer interactions: three Support VectorMachine with three different kernels (rbf, degree 2 poly-nomial, and linear), Decision Tree, K-nearest neighbors,Perceptron, Multi-layer Perceptron, and a Random Forestclassifier.

5 RESULTS

We conducted two sets of experiments for testing our pro-posed methods. The results of two personality analysis ap-proaches utilized in this research were compared with IBMWatson personality insights. IBM Watson itself utilized thepersonality score that was obtained from personality survey,as ground truth labeling. This comparison is performed dueto the similarity between our framework and IBM Watsonpersonality insight service. The Watson Personality Insightsservice extracts information from text data in user’s textmessages, tweets, and posts using linguistic analysis andperforms personality analysis. In what follows, we demon-strate the detailed results for each part of the data analysis.

5.1 Performance of PDF ApproachFor evaluating the performance of the PDF approach, wecalculated the PDF vector of each data point. Thereupon,the peak value of the vector was compared with the groundtruth label. The results show that the Mean Absolute Error(MAE) and the Root Mean Square Error (RMSE) of the PDFmethod are 15.5% and 19.5%, respectively. The logarithmicratio of the first to the second maximum was then calculatedfor each text sample for use as a measure of “confidence”.The confidence scores were from 0 to 10 with 0 being thelowest confidence and 10 the highest. In some cases, thefirst and second peaks are comparable, while in other casesthey are significantly different. An example of each is shownin Figure 7a and Figure 7b. The number of words in the textsample shown in Figure 7a are 3, 079 and the score givenby IBM Watson is 0.44. One of the peaks given by PDFapproach shows the Neuroticism value in the range of 0.4and 0.5. In Figure 7b, the number of words are 3, 500 and theNeuroticism score given by IBM Watson is 0.18. The scoregiven by the PDF approach is in the range of 0.2 and 0.3which is close to IBM score.

The MAE of the samples with confidence values morethan two is 10.5%. Figure 8 shows the MAE based ondifferent confidence levels.

(a) An example of a low confidence data point.

(b) An example of a high confidence data point.

Fig. 7: Two data points with different confidence scores.

Fig. 8: The Mean Absolute Error-Confidence plot shows byincreasing the confidence metric the MAE drops below 4%.

5.2 Performance of ML-Based Personality AssessmentSince the amount of the mined data are large, we initiallyevaluated the accuracy using a train-test split (67% training,33% testing). After choosing the best classifier, we used a 10-fold cross validation method for better accuracy evaluation.As it is shown in Table 1, the data are not well distributedover all the scores. Therefore, we chose the most evenlydistributed personality trait (Neuroticism) for performingour experiments and training models. For training ML clas-

10

TABLE 3: The distributions of the answers to some of the commonsense questions.

Question without fusion with fusionName a subject that sons discuss with their fathers rather than withtheir mothers. 5%,33%,46%,16%, 54% ,46%

President Trump’s travel ban is 25%,17%,10%,48%, 35% ,65%You share values with 26%,39%,19%,16%, 26% 39%,35%What is your opinion about creating united healthcare by insurancingthe sales/income tax? 54%,20%,14%,12%, 54% 46%

sifiers, we utilized real value scores and converted them tointeger percentages. While for training ML regressors weutilized real values as ground truth labeling, and outputof the regressors were real values. Therefore, the accuracyof a classifier was measured based on the differences ofthe output labels which were numbers in range of 1 to8, with the ground-truth labels generated by IBM Watson.The accuracy of a regressor was measured based on thedifference between the actual score in range of 0-1 and theassociated ground-truth labe.

Scikit provides the root-mean-square deviation (RMSD)or root-mean-square error (RMSE) of each trained modelwhich are frequently used as measures of the differencesbetween actual values and the values predicted by a model.RMSD is based on the standard deviation of the predic-tions from ground-truth labels. While RMSE based on theaverage differences of predictions with ground-truth labels.The main purpose of this study is to predict a score asclose as possible to the actual personality scores. There-fore, we defined an error margin for calculating the ac-curacy. If a predicted score is within the range of margin(-margin,+margin) away from the actual score, then it iscounted as a correct prediction. Otherwise, it is counted asa mistake. The margin of error is set to 10% for calculatingthe performance. It means if the score should be 90% and theprediction is in the range of 80% to 100%, this is counted as acorrect prediction. Results are shown in Table 4. The settingfor each classifier and regressor is explained as follows:

• SVM-Linear: A support vector machine with linearkernel.

• SVM-Polynomial: A support vector machine withpolynomial degree 2 kernel.

• SVM-rbf: A support vector machine with RadialBasis Function kernel. This is one of the most pop-ular kernels. For distance metric squared euclideandistance was used.

• Decision Tree Classifier: A supervised learning clas-sifier that trains decision rules for classifying thedata. In this work, the random state was set to zero.

• KNN: K-Nearest Neighbor is a supervised learningclassifier. The classification is done based on the voteof its neighbors. The class label of each datapointwill be the most common class among its k nearestneighbor. In this paper, k was assigned to 5.

• Perceptron: A supervised learning classifier, gener-ally used for binary classification.

• MLP: Multi-layer Perceptron a artificial neural net-work classifier. We utilized a log-loss function as asolver. L2 regularization penalty is set to 10−5. Thesize of hidden layers was 15.

• SVR-rbf: A support vector regressor with rbf kernel.

TABLE 4: The error metrics for each classifier

Classifiers and Regressors Marginal Error RMSE

SVM-Linear 37% 27%

SVM-Polynomial 50% 47%

SVM-rbf 39% 30%

Decision Tree 50% 34%

KNN 45% 40%

Perceptron 43% 29%

MLP 30% 22%

SVR-rbf 43% 26%

Linear Regression 40% 24%

RFR 46% 27%

RFC 35% 27%

Kernel coefficient was set to 0.1 and the penaltyparameter was set to 103.

• Linear Regression: Attempted to model a relation-ship in data by fitting a linear equation to observeddata.

• RFR: Random forest fits a number of classifyingdecision trees on the samples of the dataset and usesaveraging to improve the predictive accuracy andcontrol over-fitting. This is a random forest regressorwith the maximum depth set to two.

• RFC: Random forest classifier with the number oftrees set to 1000.

The highlighted cells are the lowest error values, obtainedby the multi-layer perceptron.

5.3 Performance of ML-Based Commonsense Reason-ingWe initially calculated the correlation between each inputfeature and the output labels and the features with low cor-relations were filtered out in building the prediction models.This results in different feature sets for each commonsensequestion. A model was trained for each commonsense ques-tion using each training model (8×67 models). For evaluat-ing the performance, a 10-fold cross validation method wasutilized. The non-uniform distribution of the class labels insome questions made the problem too easy and resultedin very high accuracies; therefore, these questions are not agood criterion for the evaluation of the models. On the otherhand, some questions are well distributed between morethan 3 class labels. Due to the low number of data points,these questions lead to significantly low accuracies. The bestcriterion for evaluating the classifiers and regressors are thequestions with uniform distribution among few numbers ofclass labels. We compared the accuracies of the classifiersfor some of the questions before and after the data fusion.

11

The results are shown in Figure 9. For instance, in Figure 9bthe choices of the questions were: 1) Great and improvesthe safety of legal and patriotic US citizens (25%); 2) Notgood, since not all Muslim people in the banned countriesshould be punished because their fellow citizens committedthe Sep.11 attack (17%); 3) Good, but should include morecountries (10%); and 4) Not good, because it is discrimina-tory (48%). We merged options 1 with 3 and options 2 with4, the distribution of the data became (65% and 35%). Theaverage accuracy before and after the data fusion 52% and76%, respectively.

(a)

(b)

(c)

Fig. 9: Accuracy values of models before and after data fusion.

TABLE 5: Confusion matrix for the commonsense question:“What is your opinion about creating united healthcare insur-ance by increasing the tax?”

XXXXXXXXXXCorrectPredicted

Agree Disagree

Agree 60 20Disagree 19 41

TABLE 6: Confusion matrix for the commonsense question:“What do you think about Trump’s travel ban?”

XXXXXXXXXXCorrectPredicted

Agree Disagree

Agree 31 19Disagree 9 81

TABLE 7: Confusion matrix for the commonsense question:“Tell me the most important habit a mom should teach herson?”

XXXXXXXXXXCorrectPredicted

Respect Others

Respect 47 21Others 30 41

The confusion matrix for each commonsense reasoningquestion is shown in Table 5 to Table 7.

6 CONCLUDING REMARKS

The methodologies proposed in this article address impor-tant problems in the area of behavioral analysis throughtwo novel approaches based on machine learning and prob-ability density functions. The proposed system may havemany different applications, including mental health careand suicide/homicide prevention. In this work, we appliedour methodologies on personality assessment and common-sense reasoning. Scores from IBM Watson Personality In-sights were utilized for ground-truth labeling. However, ourgoal in this work was to match or surpass the performanceof Watson with a system requiring far less computationalresources. The proposed approaches achieved a comparableresult to the ground truth labels. For the PDF approachwith the confidence factor of higher than 3, the accuracyreached 97%. The best results achieved by ML modelingwas 88.2% through the multilayer perceptron. In this article,we utilized 345 adjectives in order to train accurate modelsfor personality analysis. 345 adjectives were outcome of ourexperiments based on the available text. We are planningon collecting more text data and include other words suchas verbs, nouns, and pronouns in our experiments. Fur-thermore, by increasing the samples more adjectives canbe added to the list. Although 345 input features are notideal for training a model, in this work we targeted a low-complexity solution for running the proposed method onsmartphones rather than an alternative for IBM Watson thatneeds a supercomputer to run.

In this work, commonsense decisions were predictedbased upon an individual’s personality trait. For perform-ing commonsense reasoning, we proposed an approachutilizing ML techniques. We collected data from 345 peo-ple through Amazon Mechanical Turk and Facebook. Twosets of questions were provided. The first set containedthe personality analysis questions which were utilized asinput features for the commonsense reasoning problem.The second set contained the commonsense questions. Eachcommonsense question was modeled separately. In this con-text, commonsense questions had a number of reasonableand justifiable answers. Each answer was a class label for

12

training a model. Based on the evaluations, Random ForestClassifier achieved 82.3% accuracy in predicting the answersto some commonsense questions. We note that common-sense questions, which may target different political point-of-views, ethical priorities, marketing practices, and otherpersonal choices, preferences, and habits, may have a num-ber of reasonable and justifiable answers instead of a uniquefactual answer.

In this study, we performed experiments to generateconvincing evidences that the proposed low-complexity ap-proach is in fact functional. However, more complex andcomprehensive experiments are needed to assess large scaleadoption of these technologies. We focused on a compu-tationally simple method that can be easily implementedas a mobile application. Our goal in this study was not todevelop an alternative approach to the IBM Watson tool,which uses a supercomputer to run. In the future, we planto utilize a comprehensive set of word categories (e.g., verbs,nouns) throughout the text samples and use those datafor design and development of our computational models.Moreover, in this study, we utilized one personality trait inour experiments. A future study will involve collecting datafrom various sources to incorporate all five personality traitsin our experiments.

There were few sources of error in designing the al-gorithms. The number of subjects participated in the datacollection was limited so that we could not cover a largenumber of personality traits at a large scale. To train com-monsense reasoning models, we collected data throughAmazon mechanical Turk; however, we were able to provideonly a nominal amount of monetary incentive to the partic-ipants. This may have potentially impacted the quality ofthe collected data. Moreover, the number of subjects takingthe survey was limited, and the survey results may havebeen affected by many factors such as personal biases. Inour ongoing research, we are currently designing a datacollection framework, which prompts the user with ques-tions throughout the day, asking them to express their dailyactivities such as their experiences, their dietary intake, andinteractions. The inputs to this system are in form of spokenlanguage. The speech is then converted to text and utilizedfor personality analysis. For the commonsense reasoninganalysis, we plan on recruiting more participants to trainhighly accurate models for commonsense reasoning.

7 DISCUSSION AND FUTURE DIRECTION

In this work IBM Watson Personality Insight tool, whichauthors modeled by two low-complexity approaches, onlyprovides 5 FFM scores instead of providing probable re-sponses to a standard FFM questionnaire. However, thereare 550 distinct cases when a standard FFM questionnaireconsisting of 50 questions and 5 possible answers per ques-tion is filled, thus the questionnaire conveys more informa-tion than only 5 FFM percentiles. Questions in a standardFFM questionnaire, are expected to be independent andorthogonal to each other meaning that each question tar-gets a unique dimension of human personality and closelymimic Eigenvectors in an N-dimensional space. Authorsbuilt the commonsense reasoning scheme directly based onresponses to the questionnaire.

As stated, personalizing the algorithms and servicesin most efficient way has been the focus of many re-cent researches in human-centered applications [47], [70]–[72]. Therefore, assessing the personality of humans wouldbe a helpful and necessary step. This work is the firststep towards integrating personality analysis and common-sense reasoning with all the limitations that research inacademia has Nevertheless. However, it has achieved itsobjective to establish the connection between personalitytrait and common-sense reasoning. In this work, we havenot achieved the ultimate goal to directly extract common-sense reasoning from text. To achieve this ultimate goal, oneshould exceed IBM Watson and need to have access to areally large number of text samples. Although, authors didnot have access to the required resources; they highlightedthe missing parts and will pursue these in their futuredirection.

Although this study clearly showed that commonsensereasoning is related to personality trait, the authors do/didnot have the means to fill the FFM questionnaire directlyfrom a text sample. Authors believe FFM scores, that areeasier to use, convey less information than the question-naire and therefore, IBM Watson as well as our methodcannot rigorously revive the loss of information and fill thequestionnaire using FFM percentiles. This is an interestingtopic that can be explored in the future and requires a largenumber of text samples and responses to questionnaires tofacilitate predicting responses to questions based on textfeatures.

Although applications need many words to learn per-sonalities, it does not need to happen instantaneously. Theusers do not need to write an essay every time. The finalproduct can use peoples voice, text, and other modalitiesand collect data overtime. As more users adopt the systemand add more data to the applications, the performanceimproves. In our ongoing work, we are collecting data fromusers in real-time on daily basis by prompting them withspecific questions throughout the day. For example, howwas your day? What did you eat? Tell me about a movieyou watched, a book you read, or a conversation you had.Then this data is utilized for personality assessment andcommonsense reasoning. We expect that the amount of datacollected over time will be an order of magnitude greaterthan what we have right now. Huge amounts of data will beavailable to improve the performance dramatically.

In this work, the questionnaire is filled by a small num-ber of users with different personality traits, rather thana large number of people in the society. The connectionbetween a commonsense question and a specific feature inthe personality trait is established using machine learning.In general, people in the society will not need to fill aquestionnaire if their personality can be estimated by othermeans including their digital footprint.

REFERENCES

[1] H. Isselhorst, “The human factor, forcasting investor behavior,”Behavioural Finance Solutions.

[2] J. Krueger, “How illusory is the voter’s illusion?” Proceedings of ThePsychology of Voting and Election Campaigns Conference, Oct 2006.

[3] P. Foot, “The problem of abortion and the doctrine of doubleeffect,” 1967.

13

[4] J. J. Thomson, “The trolley problem,” The Yale Law Journal, vol. 94,no. 6, pp. 1395–1415, 1985.

[5] N. J. Goodall, “Can you program ethics into a self-driving car?”IEEE Spectrum, vol. 53, no. 6, pp. 28–58, 2016.

[6] B. W. Roberts, K. E. Walton, and W. Viechtbauer, “Patterns ofmean-level change in personality traits across the life course: ameta-analysis of longitudinal studies.” Psychological bulletin, vol.132, no. 1, p. 1, 2006.

[7] K. M. DeNeve and H. Cooper, “The happy personality: A meta-analysis of 137 personality traits and subjective well-being.” Psy-chological bulletin, vol. 124, no. 2, p. 197, 1998.

[8] L. R. Goldberg, “The structure of phenotypic personality traits.”American psychologist, vol. 48, no. 1, p. 26, 1993.

[9] T. Correa, A. W. Hinsley, and H. G. De Zuniga, “Who interacts onthe web?: The intersection of users personality and social mediause,” Computers in Human Behavior, vol. 26, no. 2, pp. 247–253,2010.

[10] D. J. Hughes, M. Rowe, M. Batey, and A. Lee, “A tale of two sites:Twitter vs. facebook and the personality predictors of social mediausage,” Computers in Human Behavior, vol. 28, no. 2, pp. 561–569,2012.

[11] A. Buchheim and E. Mergenthaler, “The relationship among at-tachment representation, emotion-abstraction patterns, and narra-tive style: A computer-based text analysis of the adult attachmentinterview,” Psychotherapy Research, vol. 10, no. 4, pp. 390–407, 2000.

[12] G. C. Gleser, L. A. Gottschalk, and W. John, “The relationship ofsex and intelligence to choice of words: A normative study ofverbal behavior,” Journal of Clinical Psychology, vol. 15, no. 2, pp.182–191, 1959.

[13] R. Brown, Words and Things: An Introduction to Language: with aNew Preface by the Autor. Free Press, 1968.

[14] D. S. Berry, J. W. Pennebaker, J. S. Mueller, and W. S. Hiller, “Lin-guistic bases of social perception,” Personality and Social PsychologyBulletin, vol. 23, no. 5, pp. 526–537, 1997.

[15] F. Mairesse, M. Walker et al., “Words mark the nerds: Compu-tational models of personality recognition through language,” inProceedings of the Annual Meeting of the Cognitive Science Society,vol. 28, no. 28, 2006.

[16] J. W. Pennebaker, M. R. Mehl, and K. G. Niederhoffer, “Psycho-logical aspects of natural language use: Our words, our selves,”Annual review of psychology, vol. 54, no. 1, pp. 547–577, 2003.

[17] D. Antonioni, “Relationship between the big five personalityfactors and conflict management styles,” International journal ofconflict management, vol. 9, no. 4, pp. 336–355, 1998.

[18] J. W. Pennebaker and L. A. King, “Linguistic styles: Languageuse as an individual difference.” Journal of personality and socialpsychology, vol. 77, no. 6, p. 1296, 1999.

[19] A. Beauducel, D. Liepmann, J. Felfe, and W. Nettelnstroth, “Theimpact of different measurement models for fluid and crystallizedintelligence on the correlation with personality traits,” EuropeanJournal of Psychological Assessment, vol. 23, no. 2, pp. 71–78, 2007.

[20] J. M. Digman, “Personality structure: Emergence of the five-factormodel,” Annual review of psychology, vol. 41, no. 1, pp. 417–440,1990.

[21] B. E. de Raad and M. E. Perugini, Big five assessment. Hogrefe &Huber Publishers, 2002.

[22] A. A. J. Hendriks, “The construction of the five-factor personalityinventory (ffpi),” Ph.D. dissertation, Rijksuniversiteit GroningenGroningen, 1997.

[23] C. G. DeYoung, L. C. Quilty, J. B. Peterson, and J. R. Gray,“Openness to experience, intellect, and cognitive ability,” Journalof personality assessment, vol. 96, no. 1, pp. 46–52, 2014.

[24] R. R. McCrae and O. P. John, “An introduction to the five-factormodel and its applications,” Journal of personality, vol. 60, no. 2, pp.175–215, 1992.

[25] M. Zeidner and G. Matthews, “Intelligence and personality.” 2000.[26] C. G. DeYoung, L. C. Quilty, and J. B. Peterson, “Between facets

and domains: 10 aspects of the big five.” Journal of personality andsocial psychology, vol. 93, no. 5, p. 880, 2007.

[27] Y. Amichai-Hamburger and G. Vinitzky, “Social network use andpersonality,” Computers in human behavior, vol. 26, no. 6, pp. 1289–1295, 2010.

[28] C. Ross, E. S. Orr, M. Sisic, J. M. Arseneault, M. G. Simmering, andR. R. Orr, “Personality and motivations associated with facebookuse,” Computers in human behavior, vol. 25, no. 2, pp. 578–586, 2009.

[29] T. A. Judge, J. B. Rodell, R. L. Klinger, L. S. Simon, and E. R.Crawford, “Hierarchical representations of the five-factor model

of personality in predicting job performance: integrating threeorganizing frameworks with two theoretical perspectives.” Journalof Applied Psychology, vol. 98, no. 6, p. 875, 2013.

[30] F. Drasgow and J. B. Olson-Buchanan, Innovations in computerizedassessment. Psychology Press, 1999.

[31] A. Cooper and K. Petrides, “A psychometric analysis of the traitemotional intelligence questionnaire–short form (teique–sf) usingitem response theory,” Journal of personality assessment, vol. 92,no. 5, pp. 449–457, 2010.

[32] G. Shahar, E. F. Gallagher, S. J. Blatt, G. P. Kuperminc, and B. J.Leadbeater, “An interactive-synergetic approach to the assessmentof personality vulnerability to depression: Illustration using theadolescent version of the depressive experiences questionnaire,”Journal of Clinical Psychology, vol. 60, no. 6, pp. 605–625, 2004.

[33] S. Argamon, S. Dhawle, M. Koppel, and J. Pennebaker, “Lexicalpredictors of personality type,” 2005.

[34] R. P. Hart, “Redeveloping diction: theoretical considerations,”Progress in communication sciences, pp. 43–60, 2001.

[35] C. K. Chung and J. W. Pennebaker, “Linguistic inquiry and wordcount (liwc): pronounced luke,... and other useful facts,” in Appliednatural language processing: Identification, investigation and resolution.IGI Global, 2012, pp. 206–229.

[36] J. W. P. M. E. Francis and R. J. Booth, “Linguistic inquiry and wordcount,” 1993.

[37] J. McAuley and A. Yang, “Addressing complex and subjectiveproduct-related queries with customer reviews,” in Proceedings ofthe 25th International Conference on World Wide Web. InternationalWorld Wide Web Conferences Steering Committee, 2016, pp. 625–635.

[38] R. Lambiotte and M. Kosinski, “Tracking the digital footprints ofpersonality,” Proceedings of the IEEE, vol. 102, no. 12, pp. 1934–1939,2014.

[39] D. Lazer, A. S. Pentland, L. Adamic, S. Aral, A. L. Barabasi,D. Brewer, N. Christakis, N. Contractor, J. Fowler, M. Gutmannet al., “Life in the network: the coming age of computational socialscience,” Science (New York, NY), vol. 323, no. 5915, p. 721, 2009.

[40] A. Pentland and T. Heibeck, Honest signals: how they shape our world.MIT press, 2010.

[41] J.-P. Onnela, J. Saramaki, J. Hyvonen, G. Szabo, D. Lazer, K. Kaski,J. Kertesz, and A.-L. Barabasi, “Structure and tie strengths in mo-bile communication networks,” Proceedings of the national academyof sciences, vol. 104, no. 18, pp. 7332–7336, 2007.

[42] N. Eagle, A. S. Pentland, and D. Lazer, “Inferring friendshipnetwork structure by using mobile phone data,” Proceedings of thenational academy of sciences, vol. 106, no. 36, pp. 15 274–15 278, 2009.

[43] D. J. Watts, “A twenty-first century science,” Nature, vol. 445, no.7127, p. 489, 2007.

[44] J. Teevan, “How people recall, recognize, and reuse search results,”ACM Transactions on Information Systems (TOIS), vol. 26, no. 4,p. 19, 2008.

[45] J. Golbeck, C. Robles, and K. Turner, “Predicting personality withsocial media,” in CHI’11 extended abstracts on human factors incomputing systems. ACM, 2011, pp. 253–262.

[46] D. Quercia, M. Kosinski, D. Stillwell, and J. Crowcroft, “Ourtwitter profiles, our selves: Predicting personality with twitter,” in2011 IEEE third international conference on privacy, security, risk andtrust and 2011 IEEE third international conference on social computing.IEEE, 2011, pp. 180–185.

[47] B. Ferwerda, M. Schedl, and M. Tkalcic, “Predicting personalitytraits with instagram pictures,” in Proceedings of the 3rd Workshopon Emotions and Personality in Personalized Systems 2015. ACM,2015, pp. 7–10.

[48] S. J. Russell and P. Norvig, Artificial intelligence: a modern approach.Malaysia; Pearson Education Limited,, 2016.

[49] H. Liu and P. Singh, “Conceptneta practical commonsense reason-ing tool-kit,” BT technology journal, vol. 22, no. 4, pp. 211–226, 2004.

[50] ——, “Commonsense reasoning in and over natural language,” inInternational Conference on Knowledge-Based and Intelligent Informa-tion and Engineering Systems. Springer, 2004, pp. 293–306.

[51] J. F. Sowa et al., Knowledge representation: logical, philosophical,and computational foundations. Brooks/Cole Pacific Grove, 2000,vol. 13.

[52] G. A. Miller, “Wordnet: a lexical database for english,” Communi-cations of the ACM, vol. 38, no. 11, pp. 39–41, 1995.

[53] P. Singh, T. Lin, E. T. Mueller, G. Lim, T. Perkins, and W. L.Zhu, “Open mind common sense: Knowledge acquisition fromthe general public,” in OTM Confederated International Conferences”

14

On the Move to Meaningful Internet Systems”. Springer, 2002, pp.1223–1237.

[54] E. T. Mueller, “A calendar with common sense,” in Proceedings ofthe 5th international conference on Intelligent user interfaces. ACM,2000, pp. 198–201.

[55] T. Buchanan, J. A. Johnson, and L. R. Goldberg, “Implementing afive-factor personality inventory for use on the internet,” EuropeanJournal of Psychological Assessment, vol. 21, no. 2, pp. 115–127, 2005.

[56] R. Humphrey, Stream of consciousness in the modern novel. Univ ofCalifornia Press, 1958.

[57] A. Verma, G. Dasgupta, T. K. Nayak, P. De, and R. Kothari, “Serverworkload analysis for power minimization using consolidation,”in Proceedings of the 2009 conference on USENIX Annual technicalconference. USENIX Association, 2009, pp. 28–28.

[58] S. Bird and E. Loper, “Nltk: the natural language toolkit,” inProceedings of the ACL 2004 on Interactive poster and demonstrationsessions. Association for Computational Linguistics, 2004, p. 31.

[59] D. Basak, S. Pal, and D. C. Patranabis, “Support vector regression,”Neural Information Processing-Letters and Reviews, vol. 11, no. 10, pp.203–224, 2007.

[60] M. H. Kutner, C. Nachtsheim, and J. Neter, Applied linear regressionmodels. McGraw-Hill/Irwin, 2004.

[61] A. Liaw, M. Wiener et al., “Classification and regression by ran-domforest,” R news, vol. 2, no. 3, pp. 18–22, 2002.

[62] M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf,“Support vector machines,” IEEE Intelligent Systems and their appli-cations, vol. 13, no. 4, pp. 18–28, 1998.

[63] S. R. Safavian and D. Landgrebe, “A survey of decision treeclassifier methodology,” IEEE transactions on systems, man, andcybernetics, vol. 21, no. 3, pp. 660–674, 1991.

[64] L. E. Peterson, “K-nearest neighbor,” Scholarpedia, vol. 4, no. 2, p.1883, 2009.

[65] Y. Freund and R. E. Schapire, “Large margin classification usingthe perceptron algorithm,” Machine learning, vol. 37, no. 3, pp. 277–296, 1999.

[66] M. W. Gardner and S. Dorling, “Artificial neural networks (themultilayer perceptron)a review of applications in the atmosphericsciences,” Atmospheric environment, vol. 32, no. 14-15, pp. 2627–2636, 1998.

[67] M. Pal, “Random forest classifier for remote sensing classifica-tion,” International Journal of Remote Sensing, vol. 26, no. 1, pp.217–222, 2005.

[68] I. E. Allen and C. A. Seaman, “Likert scales and data analyses,”Quality progress, vol. 40, no. 7, p. 64, 2007.

[69] A. McClintock, “Family feuds: Gender, nationalism and the fam-ily,” Feminist review, vol. 44, no. 1, pp. 61–80, 1993.

[70] Z. Esna Ashari and H. Ghasemzadeh, “Mindful active learning,”in 2019 IJCAI conference (International Joint Conference on ArtificialIntelligence), Aug 2019.

[71] M. Pedram, M. Rofouei, F. Fraternali, Z. Esna Ashari, andH. Ghasemzadeh, “Resource-efficient computing in wearable sys-tems,” in 2019 IEEE International Conference on Smart Computing(SmartComp), Jun 2019.

[72] Y. Ma, Z. Esna Ashari, M. Pedram, N. Amini, D. Tarquinio,K. Nouri-Mahdavi, M. Pourhomayoun, R. D. Catena, andH. Ghasemzadeh, “Cyclepro: A robust framework for domain-agnostic gait cycle detection,” IEEE Sensors Journal, vol. 19, no. 10,pp. 3751–3762, 2019.

15

APPENDIX ASURVEY PERSONALITY QUESTIONS

I see myself as someone ... Very Inaccu-rate

ModeratelyInaccurate

NeitherInaccuratenor Accurate

ModeratelyAccurate

VeryAccurate

who is generally trusting � � � � �Don’t like to draw attention to myself � � � � �

Believe in the importance of art � � � � �tends to find fault in others � � � � �

pays attention to details � � � � �Shirks duties � � � � �

who is generally trusting � � � � �who is reserved � � � � �

has a good word for everyone � � � � �insults people � � � � �

feels comfortable around people � � � � �dislikes self � � � � �

has a little to say � � � � �has frequent mood swings � � � � �

often feels blue � � � � �does not like art � � � � �

cuts others to pieces � � � � �is the life of the party � � � � �

believes that others have good intentions � � � � �avoids philosophical discussions � � � � �

makes friends easily � � � � �gets chores done rightaway � � � � �accepts people as they are � � � � �

relaxed, handles stress well � � � � �does a thorough job � � � � �

does just enough work to get by � � � � �enjoys going to art museums � � � � �

often down in the dumps � � � � �always prepared � � � � �

has few artistic interests � � � � �not easily bothered by things � � � � �

knows how to captivate people � � � � �is not interested in abstract ideas � � � � �makes plans and sticks to them � � � � �

doesn’t see things as they are � � � � �is outgoing and sociable � � � � �

doesn’t talk a lot � � � � �gets back at others � � � � �

tends to vote for the liberal candidates � � � � �has an active imagination � � � � �

seldoms feel blue � � � � �carries out the plans � � � � �gets nervous easily � � � � �

feels comfortable with self � � � � �panics easily � � � � �

keeps in the background � � � � �finds it difficult to get down to work � � � � �

tends to vote for conservative candidates � � � � �skilled in handling social situations � � � � �

wastes time � � � � �tends to be lazy � � � � �respects others � � � � �

16

TABLE 8

17

APPENDIX BCOMMONSENSE SURVEY AND RESULTS

Question RFC DT KNN SVM-Linear SVM-Poly SVM-rbf Perceptron MLP

What are your gender/color priori-

ties for the US commander in chief?84% 70% 83% 77% 77% 83% 83% 72%

President Trumps travel ban is 82% 72% 78% 76% 77% 79% 70% 71%

When you’re looking for work,

name a way you might find out

about the job opening

78% 66% 75% 79% 75% 79% 58% 70%

An engineer who lost her father

in childhood in a shooting accident

hates guns and blames gunmakers

for her loss.However, the only job of-

fer that she has received is from a big

gunmaker company. If she declines

the job offer (refusing to make guns

that can kill other kids’ dads), her

retired mother must sell her house to

pay off her school loan.She should ...

78% 66% 75% 79% 75% 79% 58% 70%

Name something your body tells

you it’s time to do.73% 59% 70% 70% 70% 72% 47% 64%

What is your opinion about creat-

ing united health care insurance by

slightly increasing the sales/income

tax?

73% 59% 70% 70% 70% 72% 47% 64%

You share values with 70% 59% 54% 64% 59% 66% 47% 57%

Name something that people like to

do in their backyard70% 59% 54% 64% 59% 66% 47% 57%

Name something that’s embarrass-

ing to fall asleep while doing.69% 61% 65% 65% 65% 69% 63% 60%

A patient who has agreed to do-

nate his vital organs is in a coma

and lives only with the assistance of

life-support machines,but will pass

away in the next few days anyway.

There are a few patients who need

the organs withing a few hours to

survive. Which one do you agree

with:

67% 54% 64% 65% 62% 67% 62% 68%

Name a reason people change their

names66% 54% 65% 60% 61% 66% 59% 61%

Why did Hillary Clinton lose the

presidential election?65% 59% 57% 62% 61% 65% 58% 58%

If a scientific theory does not agree

with your religious belief:65% 55% 60% 61% 65% 65% 58% 56%

Tell me the most important habit a

mom should teach her son.63% 54% 56% 64% 62% 60% 55% 60%

Name something that people do to

their faces62% 56% 57% 62% 59% 65% 58% 62%

If you were arrested and could make

only one call, whom would you call?62% 50% 56% 59% 58% 62% 59% 50%

Name something you’ve stayed up

all night worrying about.61% 55% 56% 55% 54% 59% 53% 52%

Is this a big deal if the fired missile

from Yemen to Saudi Arabia was in

fact made in Iran?

60% 54% 57% 59% 59% 61% 44% 58%

18

A US-based solar panel manufactur-ing company can no longer competewith fully-automated companies inAsia that manufacture solar panels50% cheaper. Hundreds of workerswill lose their jobs in the US if thecompany is closed. The companyshould ...

60% 55% 56% 58% 60% 63% 60% 55%

Name something that your boss

could tell you that would come as

a big shock.

60% 55% 54% 55% 55% 55% 52% 52%

In a smartphone, the main issue for

you is59% 55% 57% 59% 60% 59% 50% 56%

Name something people do when

riding in the back of a taxicab59% 53% 52% 57% 55% 57% 57% 43%

If money is not an issue, you would

like to buy59% 55% 51% 50% 54% 57% 54% 50%

Name something specific you might

remember about one of your teach-

ers from high school.

58% 52% 53% 59% 59% 59% 53% 53%

Name a reason a young man might

join the army57% 52% 51% 54% 56% 57% 50% 55%

Name a reason you might ask the

manager of a hotel for another room.57% 53% 55% 62% 60% 60% 58% 57%

You are tired of a driver who is tail-

gating you for over an hour, what

would you do?

57% 54% 56% 56% 56% 58% 58% 55%

Name a way you can tell that your

child is lying57% 52% 53% 45% 48% 54% 53% 50%

If you have ever been fined for any

driving violations, it has most likely

been for ...

57% 48% 57% 56% 58% 57% 52% 59%

The politician who wins the election

is usually the person with the most

what?

57% 52% 56% 53% 54% 56% 48% 49%

Name something that you’d like todo all day without feeling any guilt. 56% 51% 54% 53% 55% 58% 56% 51%

Which food do you think should bechosen as the ”National Food” ofAmerica?

56% 49% 56% 55% 57% 56% 54% 52%

Name something most people cleanat least once a day. 55% 55% 54% 55% 52% 54% 50% 52%

Name a reason why people some-times need to take a day off from thework.

54% 51% 53% 52% 51% 58% 54% 54%

What’s the hardest part of your bud-get to cut out? 54% 39% 52% 43% 47% 54% 33% 54%

Name a tip people give you on how

to get rich.53% 52% 52% 49% 48% 56% 49% 52%

Where would you look to find out

how old someone is?52% 53% 51% 55% 54% 55% 49% 53%

Name a technique people use to re-

member things.51% 50% 48% 48% 50% 51% 53% 52%

Name something that’s important to

parents when house hunting?50% 39% 42% 49% 46% 50% 46% 38%

Tell me a reason why a man would

want to have a son.50% 48% 49% 51% 49% 52% 49% 48%

Name a reason you might make

your spouse get out of bed at the

middle of the night

48% 43% 37% 44% 42% 40% 39% 39%

19

Many jobs will be lost in the next

few decades due to smart robots and

unemployment will thus increase.

Therefore,

48% 36% 39% 40% 41% 48% 39% 37%

When you pick a password for your

phone, you pick a password that is.48% 31% 41% 40% 40% 48% 37% 42%

On a sunny day, in a freeway withthe maximum speed of 60 miles perhour if there is no police officer, youwill typically drive with a speed ...

50% 51% 52% 50% 52% 50% 48% 57%

If money is not an issue, you willbuy a driver-less car 47% 43% 39% 42% 44% 48% 45% 42%

What should the US do if welearn that the false nuclear alarm inHawaii was real but the North Ko-rean missile exploded before reach-ing Hawaii:

47% 39% 44% 40% 41% 48% 35% 41%

If you are not in a hurry, which routewould you choose to your destina-tion:

45% 37% 34% 39% 40% 47% 40% 35%

True democracy can be implementedusing smartphone voting, as peoplecan vote every day on important is-sues and there is no need to have acostly congress or senate anymore.The people will finally be in charge!

43% 35% 36% 37% 36% 41% 38% 38%

You really like a luxury car thatcosts $70,000 but you can only afford$40,000.

43% 34% 37% 33% 35% 43% 43% 38%

Name something you hope younever find in your house. 40% 24% 34% 30% 32% 39% 24% 29%

Name a subject that sons discusswith their fathers rather than withtheir mothers.

40% 37% 39% 43% 39% 45% 30% 38%

Besides being fast, name somethingpeople like about fast food. 39% 35% 35% 35% 36% 40% 36% 35%

Name something one family mem-

ber might steal from another.37% 38% 30% 32% 32% 37% 34% 33%

A study at the University of Wash-

ington showed that by increasing the

minimum wage, unemployment rate

will increase, therefore

37% 29% 36% 33% 34% 37% 29% 34%

Name a reason you might pull your

car over to the side of the road.37% 36% 36% 30% 30% 37% 26% 31%

Name something that would be

smart to know how to ask for in a

foreign country.

37% 35% 33% 36% 32% 34% 36% 32%

Name something you do while driv-

ing, even though you know it’s

wrong

36% 32% 35% 34% 34% 40% 31% 33%

As micro sized robots will be de-

veloped soon,people can be filmed

anytime and anywhere without their

knowledge, which will destroy their

privacy.

35% 26% 34% 36% 36% 37% 32% 31%

Name something people do to guard

against wrinkles34% 27% 28% 31% 31% 34% 32% 36%

Name a reason why someone might

go a whole day without eating.33% 36% 34% 36% 31% 34% 34% 34%

Name a Doctor’s order people never

take seriously.32% 25% 27% 31% 33% 32% 29% 30%

We asked 100 married women... If

your husband asked you for a di-

vorce Sunday night, what’s the first

thing you’d do Monday morning?

30% 30% 27% 28% 26% 29% 31% 31%

20

Name a question a person who’s

afraid of flight might ask a flight

attendant.

30% 24% 26% 22% 23% 32% 30% 32%

If time flies when you’re having fun,

name a place it crawls29% 24% 30% 32% 30% 30% 26% 29%

Name something specific everyone

complains about21% 18% 22% 23% 18% 25% 16% 27%

TABLE 9

Que

stio

nA

nsw

ers

Nam

ea

subj

ectt

hats

ons

disc

uss

wit

hth

eir

fath

ers

rath

erth

anw

ith

thei

rm

othe

rs.

•G

irls

•Sp

orts

•Be

com

ing

am

an•

Bird

san

dbe

esN

ame

ate

chni

que

peop

leus

eto

rem

embe

rth

ings

.•

Thi

nkin

gab

outi

t•

Rep

eati

ngit

•W

ord

asso

ciat

ion

•St

ring

onfin

ger

•W

riti

ngit

dow

nN

ame

som

ethi

ngpe

ople

doto

guar

dag

ains

twri

nkle

s•

Boto

x•

Stay

outo

fsu

n•

Iron

clot

hes

•Su

nscr

een

•Fa

cial

crea

m•

Wea

rm

akeu

pN

ame

som

ethi

ngpe

ople

like

todo

inth

eir

back

yard

•G

ames

•Ba

rbec

ue•

Cat

chso

me

rays

•G

arde

n/M

owgr

ass

•Sw

imN

ame

som

ethi

ngpe

ople

dow

hen

ridi

ngin

the

back

ofa

taxi

cab

•Ta

lkto

the

cabb

y•

Look

outt

hew

indo

w•

Talk

once

ll•

Giv

edi

rect

ions

•R

ead

•W

atch

met

er•

Slee

pN

ame

aqu

esti

ona

pers

onw

ho’s

afra

idof

fligh

tmig

htas

ka

fligh

tatt

enda

nt.

•W

illw

ecr

ash?

•Yo

uce

rtifi

ed?

•Is

the

pilo

tgoo

d?•

How

long

isth

efli

ght?

•H

ow’s

the

wea

ther

•Is

the

plan

esa

fe?

We

aske

d10

0m

arri

edw

omen

...If

your

husb

and

aske

dyo

ufo

ra

divo

rce

Sund

ayni

ght,

•C

lean

outt

heba

nkw

hat’s

the

first

thin

gyo

u’d

doM

onda

ym

orni

ng?

•K

ick

him

out

•Le

ave

the

hous

e•

Ask

him

why

•Se

ea

law

yer/

File

•C

hang

eth

elo

cks

Whe

nyo

u’re

look

ing

for

wor

k,na

me

aw

ayyo

um

ight

find

outa

bout

the

job

open

ning

•N

ewsp

aper

•Fr

iend

•In

tern

et•

Une

mpl

oym

ento

ffice

21

Nam

eso

met

hing

you

dow

hile

driv

ing,

even

thou

ghyo

ukn

owit

’sw

rong

•Sp

eed

•Sm

oke

ciga

rett

e•

Not

use

the

seat

belt

s•

Eat/

Dri

nk•

Don

’tus

eth

esi

gnal

s•

Talk

onth

eph

one

Nam

ea

doct

or’s

orde

rpe

ople

neve

rta

kese

riou

sly

•St

opsm

okin

g•

Get

exer

cise

•Pl

enty

ofre

st•

Wei

ght/

Eatr

ight

•M

edic

ine/

vita

min

sW

here

wou

ldyo

ulo

okto

find

outh

owol

dso

meo

neis

?•

Lice

nse/

wal

let

•Bi

rth

cert

ifica

te•

Thei

rha

nds

•Th

eir

eyes

•A

skfa

mily

mem

bers

•A

skth

emN

ame

som

ethi

ngth

atw

ould

besm

artt

okn

owho

wto

ask

for

ina

fore

ign

coun

try

•Th

eem

bass

y•

Food

•D

irec

tion

s•

Bath

room

•H

elp

Nam

eso

met

hing

that

wou

ldbe

smar

tto

know

how

toas

kfo

rin

afo

reig

nco

untr

y•

The

emba

ssy

•Fo

od•

Dir

ecti

ons

•Ba

thro

om•

Hel

pN

ame

som

ethi

ngth

at’s

impo

rtan

tto

pare

nts

whe

nho

use

hunt

ing?

•Lo

cati

on•

Back

yard

•Sa

fety

•Sc

hool

•En

ough

room

sBe

side

sbe

ing

fast

,nam

eso

met

hing

peop

lelik

eab

outf

astf

ood.

•D

on’t

cook

/cle

an•

Tast

e/fla

vor

•V

arie

ty•

Its

hot

•Pr

ice/

Che

ap•

Easy

/C

onve

nien

tIf

you

wer

ear

rest

edan

dco

uld

mak

eon

lyon

eca

ll,w

hom

wou

ldyo

uca

ll?•

bail

bond

sman

•Si

blin

g•

frie

nd•

part

ner/

spou

se•

pare

nt•

law

yer

22

Ifyo

urce

llph

one

ring

sw

hile

you’

reat

chur

ch,w

ho’d

bett

erbe

calli

ng•

My

sibl

ing

•M

yki

ds•

The

lord

•T

hedo

ctor

•M

ysp

ouse

•M

ybo

ss•

My

mot

her

Tell

me

the

mos

tim

port

anth

abit

am

omsh

ould

teac

hhe

rso

n•

Res

pect

for

wom

en•

Pick

upaf

ter

self

•To

ilete

tiqu

ette

•H

ones

ty•

Man

ner

•C

lean

lines

sN

ame

som

ethi

ngth

atpe

ople

doto

thei

rfa

ces

•H

ave

faci

al•

Puto

nm

akeu

p•

Shav

e•

Get

boto

x•

Was

hN

ame

are

ason

ayo

ung

man

mig

htjo

inth

ear

my

•D

raft

ed•

Car

eer

•Ed

ucat

ion

•Se

rve

his

coun

try

•Be

allh

eca

nbe

Nam

ea

reas

onpe

ople

chan

geth

eir

nam

es•

Div

orce

s•

Dis

like

thei

rre

alna

me

•G

etm

arri

ed•

Hid

eN

ame

som

ethi

ngyo

u’d

like

todo

alld

ayw

itho

utfe

elin

gan

ygu

ilt•

Shop

ping

•W

atch

ing

TV

/M

ovie

s•

Slee

ping

/Rel

axin

g•

Rea

ding

•Ea

ting

Nam

ea

way

you

can

tell

that

your

child

isly

ing.

•Lo

okin

the

eye

•St

utte

ring

•Fi

dget

•Tu

rnre

d•

Expr

essi

on•

No

eye

cont

act

Nam

ea

reas

onw

hype

ople

som

etim

esne

edto

take

ada

yof

ffro

mth

ew

ork

•St

ress

relie

f•

Tire

d•

Han

gove

r•

Sick

23

Nam

ea

reas

onyo

um

ight

mak

eyo

ursp

ouse

geto

utof

bed

atth

em

iddl

eof

the

nigh

t•

Get

agl

ass

ofw

ater

•C

heck

the

nois

e•

Snor

ing

•Fe

elsi

ck•

Att

end

toth

eba

byN

ame

som

ethi

ngyo

urbo

dyte

llsyo

uit

’sti

me

todo

•Ea

t•

Slee

p•

Wak

eup

•G

oto

bath

room

Nam

eso

met

hing

that

your

boss

coul

dte

llyo

uth

atw

ould

com

eas

abi

gsh

ock.

•Pr

omot

ion/

rais

e•

Buis

ness

clos

ing

•Bo

ssis

gay

•Yo

udo

bad

wor

k•

You’

refir

edIf

tim

efli

esw

hen

you’

reha

ving

fun,

nam

ea

plac

eit

craw

ls•

Jail

•In

the

traf

fic•

DM

V•

Wor

k•

Chu

rch

•Sc

hool

Wha

t’sth

eha

rdes

tpar

tofy

our

budg

etto

cuto

ut?

•Sh

oppi

ng/

clot

hes

•En

tert

ainm

ent

•R

ent/

Mor

tgag

e•

Food

/Eat

ing

out

Whi

chfo

oddo

you

thin

ksh

ould

bech

osen

asth

e”N

atio

nalF

ood”

ofA

mer

ica?

•Fr

ies

•Pi

zza

•Fr

ied

chic

ken

•H

otdo

g•

Ice

crea

m•

Turk

ey•

Burg

erN

ame

som

ethi

ngsp

ecifi

cev

eryo

neco

mpl

ains

abou

t•

Taxe

s•

Traf

fic•

Wea

ther

•M

oney

•W

ork

Nam

eso

met

hing

that

’sem

barr

assi

ngto

fall

asle

epw

hile

doin

g•

Mak

ing

love

•Ea

ting

•D

rink

ing

•Ta

lkin

g/ph

one

•D

rivi

ngN

ame

som

ethi

ngsp

ecifi

cyo

um

ight

rem

embe

rab

outo

neof

your

teac

hers

from

high

scho

ol.•

Nic

e/go

odte

ache

r•

Goo

dlo

okin

g•

Way

they

dres

sed

•T

heir

glas

ses

•M

ean/

yelle

d

24

Nam

eso

met

hing

mos

tpeo

ple

clea

nat

leas

tonc

ea

day.

•H

ands

•Ba

thro

om•

Dis

hes/

Sink

•Fa

ce•

Teet

h•

Body

Nam

eso

met

hing

one

fam

ilym

embe

rm

ight

stea

lfro

man

othe

r.•

Food

•M

oney

•Lo

ver

•C

loth

es/S

hoes

•Je

wle

ryN

ame

som

ethi

ngsp

ecifi

cev

eryo

neco

mpl

ains

abou

tNam

ea

reas

onyo

um

ight

ask

the

man

ager

ofa

hote

lfor

anot

her

room

.•

Bigg

erbe

d/ro

om•

The

rmos

tatb

roke

n•

No

TV

/Cab

le•

Bed/

room

dirt

y•

Noi

sy•

Nee

dno

nsm

okin

gN

ame

ati

ppe

ople

give

you

onho

wto

getr

ich.

•W

ork

hard

•Sa

vem

oney

•G

ambl

e/lo

tter

y•

Inve

stN

ame

are

ason

why

som

eone

mig

htgo

aw

hole

day

wit

hout

eati

ng.

•Fa

stin

g/di

etin

g•

Forg

ot/b

usy

•D

epre

ssed

/str

ess

•Si

ckN

ame

are

ason

you

mig

htpu

llyo

uca

rov

erto

the

side

ofth

ero

ad.

•Fl

atti

re•

Phon

e•

Polic

e•

Out

ofga

s•

Am

bula

nce/

Fire

Nam

eso

met

hing

you

hope

you

neve

rfin

din

your

hous

e.•

Snak

e•

Inse

ct•

Dea

dbo

dy•

Rod

ent

•Bu

rgla

r•

Spou

sech

eati

ngTh

epo

litic

ian

who

win

sth

eel

ecti

onis

usua

llyth

epe

rson

wit

hth

em

ostw

hat?

•Vo

te•

Mon

ey•

Cha

rism

aTe

llm

ea

reas

onw

hya

man

wou

ldw

antt

oha

vea

son.

•To

play

spor

ts•

Tobe

like

him

•To

carr

yon

nam

e•

Tohe

lpw

hen

old

•To

love

25

Nam

eso

met

hing

that

you’

vest

ayed

upal

lnig

htw

orry

ing

abou

t.•

Kid

s/fa

mily

•Sc

hool

/Tes

t•

Hea

lth

issu

es•

Mon

ey/

Bills

•Jo

b/in

terv

iew

An

engi

neer

who

lost

her

fath

erin

child

hood

ina

shoo

ting

acci

dent

and

blam

esgu

nmak

ers

for

her

loss

.•

Acc

ept,

look

for

othe

rjo

bsH

owev

er,t

heon

lyjo

bof

fer

that

she

has

rece

ived

isfr

oma

gunm

aker

com

pany

.•

Dec

line,

ask

her

mot

her

tose

llth

eho

use

topa

yof

fthe

loan

Ifsh

ede

clin

esth

ejo

bof

fer

(ref

usin

gto

mak

egu

nsth

atca

nki

llot

her

kids

’dad

s),

•D

elin

e,lo

okfo

ran

othe

rjo

b,pa

ylo

anw

ith

crei

tcar

dhe

rre

tire

dm

othe

rm

usts

ellh

erho

use

topa

yof

fhe

rsc

hool

loan

.She

shou

ld•

Dec

line,

look

for

anot

hejo

b,do

notp

ayof

fthe

loan

AU

S-ba

sed

sola

rpa

nelm

anuf

actu

ring

com

pany

can

nolo

nger

com

pete

•Lo

bby

and

add

tarr

ifs

toim

port

edso

lar

pane

lsw

ith

fully

-aut

omat

edco

mpa

nies

inA

sia

that

man

ufac

ture

sola

rpa

nels

50%

chea

per.

•sp

read

fals

ein

fore

gard

ing

child

abus

ela

bor

inA

sia

Hun

dred

sof

wor

kers

will

lose

thei

rjo

bsin

the

US

ifth

eco

mpa

nyis

clos

ed.T

heco

mpa

nysh

ould

•H

ire

low

wag

ein

tern

atio

nalw

orke

rs•

Becl

osed

and

wor

kers

mov

eto

bett

erjo

bsIn

asm

artp

hone

,the

mai

nis

sue

for

you

is•

Spee

d•

Mem

ory

size

•D

ispl

ayqu

alit

yan

dsi

ze•

Batt

ery’

slif

espa

nTh

roug

ha

smal

linc

reas

ein

sale

s/in

com

eta

x,a

univ

ersa

lhea

lthc

are

insu

ranc

epl

an•

No

Uni

vers

alhe

alth

care

isn’

tgoo

dfo

rhi

ghqu

alit

ym

edic

alse

rvic

esca

nbe

crea

ted

inth

eU

S.D

oyo

ulik

eth

isid

ea?

•Ye

s,th

isis

agr

eati

dea!

•N

oun

ives

alhe

alth

care

isgo

od,b

utth

eta

xes

are

alre

ady

high

•N

o,U

nive

salh

ealt

hcar

eis

notg

ood

relig

ous

peop

lesh

ould

n’tp

ayto

fund

abor

tion

med

ical

bills

for

adr

unk

driv

erYo

ure

ally

like

alu

xury

car

that

cost

s$7

0,00

0,bu

tyou

can

only

affo

rd$4

0,00

0.•

you

leas

eca

rsun

till

you

can

buy

the

luxu

ryca

r•

You

buy

the

best

new

car

that

you

can

affo

rdfo

r$4

0,00

0•

You

just

buy

the

car

you

need

,for

less

than

$40,

000

•Yo

ubu

ya

used

mod

elof

the

luxu

ryca

rfo

r$4

0,00

0.W

hen

you

pick

apa

ssw

ord

for

your

phon

e,yo

upi

ckon

eth

aris

•Ea

syto

rem

embe

ran

dke

epit

unch

ange

d•

Diffi

cult

tore

mem

ber

and

keep

itun

chan

ged

•D

ifficu

ltto

rem

embe

ran

dch

ange

itre

gula

rly

•Ea

syto

rem

embe

ran

dyo

uch

ange

itre

gula

rly

You

shar

eva

lues

wit

h•

Dem

ocra

tic

part

y•

Rep

ublic

anpa

rty

•In

depe

nden

t/G

reen

part

y•

Non

eIf

mon

eyis

nota

nis

sue,

you

wou

ldlik

eto

buy

•Sa

msu

ngsm

artp

hone

s•

App

leip

hone

•H

TC

smar

tpho

nes

•LG

smar

tpho

nes

Ifno

tin

ahu

rry,

whi

chro

utyo

uch

oose

?•

wit

hle

astt

raffi

c•

Shor

test

/fas

test

•m

osts

ceni

c•

best

/saf

est

26

Adr

iver

ista

ilgat

ing

you

for

over

anho

ur,w

hatw

ould

you

do?

•ig

nore

itan

dre

mai

npa

tien

t•

Spee

dup

and

leav

eit

behi

nd•

slow

dow

nan

dan

noy

the

driv

er•

Pull

over

and

leti

tpas

sW

hydi

dH

illar

yC

linto

nlo

seth

epr

esid

enti

alel

ecti

on?

•R

ussi

ancy

bera

ttac

ksan

dfa

kene

ws

•U

Sel

ecto

rals

yste

m•

Vote

rsdi

dn’t

like

her

•FB

Ian

noun

cem

entr

egar

din

new

emai

lson

ala

ptop

Wha

tsho

uld

the

US

doif

we

lear

nth

atth

efa

lse

nucl

ear

alar

min

Haw

aiiw

asre

al•

Do

noth

ing

asno

harm

was

caus

edbu

tthe

Nor

thK

orea

nm

issi

leex

plod

edbe

fore

reac

hing

Haw

aii:

•D

etro

yN

orth

Kor

eaby

anu

clea

rat

tack

•A

ssas

sina

teN

orth

Kor

ean

lead

er•

Add

mor

esa

ncti

ons

Wha

tare

your

gend

er/c

olor

prio

riti

esfo

rth

eU

Sco

mm

ande

rin

chie

f?•

Col

oran

gend

erdo

notm

atte

r•

Col

orm

atte

rsbu

tgen

der

does

not

•G

ende

rm

atte

rsbu

tcol

ordo

esno

t•

Gen

der

and

colo

rm

atte

rIf

mon

eyis

nota

nis

sue,

you

will

buy

adr

iver

-les

sca

r•

Rig

htaw

ay•

Onl

yw

hen

25%

ofca

rsar

edr

iver

-les

s.•

Onl

yw

hen

50%

ofca

rsar

edr

iver

-les

s•

Onl

yw

hen

you

have

noot

her

opti

onPr

esid

entT

rum

pstr

avel

ban

is•

Gre

atan

dim

prov

esth

esa

fety

ofle

gala

ndpa

trio

tic

US

citi

zens

•N

otgo

od,b

ecau

seit

isdi

scri

min

ator

y.•

Not

good

,sin

ceno

tall

Mus

limpe

ople

inth

eba

nned

coun

trie

ssh

ould

bepu

nish

edbe

caus

eof

the

Sept

.11

atta

ck•

Goo

d,bu

tsho

uld

incl

ude

mor

eco

untr

ies

Isth

isa

big

deal

ifth

efir

edm

issi

lefr

omYe

men

toSa

udiA

rabi

a•

No,

mis

sile

sco

uld

have

been

sent

befo

reth

esa

ncti

ons

was

infa

ctm

ade

inIr

an?

•N

o,Sa

udiA

rabi

aus

esU

Sbu

iltm

issi

les

•Ye

s,it

show

svi

olat

ion

insa

ncti

ons

•Ye

s,it

show

sIr

anhe

lped

Yem

eto

atta

ckSa

udiA

rabi

aIf

asc

ient

ific

theo

rydo

esno

tagr

eew

ith

your

relig

ious

belie

f•

You

deny

the

scie

ntifi

cth

eory

,as

scie

nce

isno

tflaw

less

.•

You

dono

tpra

ctic

ean

yre

ligio

n.•

You

conv

ertt

oa

new

relig

ion

that

agre

esw

ith

that

theo

ry.

•Yo

upr

acti

ceyo

urre

ligio

nbu

tliv

ew

ith

the

diff

eren

ces.

Ast

udy

atth

eU

nive

rsit

yof

Was

hing

ton

show

edth

atby

incr

easi

ngth

em

inim

umw

age

•T

hest

udy

isfla

wed

,une

mpl

oym

entr

ate

will

incr

ease

,the

refo

re•

The

min

imum

wag

esh

ould

befu

rthe

rin

crea

sed

tore

duce

unem

ploy

emen

t•

The

min

imum

wag

esh

ould

bere

duce

d•

The

resh

ould

bein

com

eta

xto

supp

ortu

nem

ploy

edpe

ople

.M

any

jobs

will

belo

stin

the

next

few

deca

des

due

tosm

artr

obot

s•

No

idea

!an

dun

empl

oym

entw

illth

usin

crea

se.T

here

fore

,•

Cap

ital

ism

will

colla

pse,

sinc

eth

em

iddl

ecl

ass

will

vani

sh•

Smar

trob

ots

shou

ldbe

bann

edto

save

jobs

.•

The

gove

rnm

ents

houl

dpr

ovid

ea

“liv

ing

wag

e”in

com

efo

ral

lune

mpl

oyed

wor

kers

.

27

As

mic

rosi

zed

robo

tsw

illbe

deve

lope

dso

on,p

eopl

eca

nbe

film

edan

ytim

e•

Peop

lew

illle

arn

topr

otec

tthe

irpr

ivac

y.an

dan

ywhe

rew

itho

utth

eir

know

ledg

e,w

hich

will

dest

roy

thei

rpr

ivac

y•

Peop

lew

illst

opm

icro

-siz

edro

bots

.•

The

mea

ning

ofpr

ivac

yw

illch

ange

•Pe

ople

will

tole

rate

the

loss

ofpr

ivac

y.Tr

uede

moc

racy

can

beim

plem

ente

dus

ing

smar

tpho

nevo

ting

,•

Ther

ew

illbe

nore

aldi

ffer

ence

.Dem

ocra

cyis

nott

hean

swer

.as

peop

leca

nvo

teev

ery

day

onim

port

anti

ssue

s,•

You

pref

era

com

bina

tion

ofsm

artp

hone

voti

ngan

dtr

adit

iona

lrep

rese

ntat

ives

and

ther

eis

none

edto

have

aco

stly

cong

ress

orse

nate

anym

ore.

•Yo

upr

efer

trad

itio

nalr

epre

sent

ativ

esth

atyo

uca

ntr

usta

ndvo

tes

The

peop

lew

illfin

ally

bein

char

ge!

•Th

isis

agr

eati

dea.

Why

shou

ldw

epa

yto

keep

abr

oken

syst

em?