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The How, When and Why of Sentiment Analysis
Mrs. Vijyalaxmi M*, Mrs Shalu Chopra*, Mrs Sangeeta Oswal*,Mrs Deepshikha Chaturvedi* * Departement of Information Technology, VESIT,Mumbai
Abstract— With the explosive growth of social
media on web ,individuals and organizations are
increasingly using the content in these media for
decision making. Nowadays if one wants to buy a
consumer product one prefer user reviews and
discussion in public forums on web about the
product.As a result opinion mining has gained
importance. This online -word-of-mouth represent
new and measurable source of information with
many applications, this process of identifying and
extracting subjective information from raw data is
known as sentiment analysis. This paper presents a
survey on sentiment analysis or opinion mining.
The existing literature of opinion mining is
explained in detail. We explore the application
oriented approach and discussed various domain
where opinion mining would be useful. Finally we
put forward research challenges in this field.
1. INTRODUCTION
Nowadays, social media has become a platform for
people to convey their voice to the public. The
Internet has rapidly advanced from a static to an
interactive medium. Today‟s users cannot only
obtain information but also actively generate
content. News reports, BBS, forums, blogs, and etc
are the main sources of public opinion information.
The text contains both facts and opinion which
could be extracted using natural language
processing to get some opinionated
views .Opinions are usually subjective expressions
that describe people‟s sentiments, appraisals or
feelings toward entities, events and their properties,
it is a sub-discipline of computational linguistics
that focuses on extracting people‟s opinion from
the web.
When it comes to sentiment or opinion or
emotion we are not concerned with the topic of the
text but the positive or negative opinion it express.
People can freely express their opinion in social
media as reviews, blogs, micro blogs, and forum
discussion, social network sites towards any
product, service, news or organization. All these
platforms provide a huge amount of valuable
information that we are interested to analyze.
Given a piece of text, opinion-mining systems
analyze:
· Which part is opinion expressing?
· Who wrote the opinion?
· What is being commented?
Sentiment analysis, on the other hand, is about
determining the subjectivity, polarity (positive or
negative) and polarity strength (weakly positive,
mildly positive, strongly positive, etc.) of a piece
of text.
Sentiment analysis has found its application in
almost every domain. Any individual wants to buy
a product or use a service will check the opinion or
reviews of the others. Also the organization would
like to know the sentiment of its customers for
improving the service or product.
2. LITERATURE SURVEY
Sentiment analysis can be done at three levels
namely; Document level, Sentence level, Entity
and aspect level. Document level expresses the
positive or negative opinion of a single entity in
the document as a whole. In sentence level each
sentence in the document is analyzed to determine
the positive, negative or neutral opinion [1]. An
aspect-based opinion polling system takes as input
a set of textual reviews and some predefined
aspects, and identifies the polarity of each aspect
from each review to produce an opinion poll [2]. It
Sangeeta Oswal et al, Int.J.Computer Technology & Applications,Vol 4 (4),660-665
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ISSN:2229-6093
is based on the idea that an opinion consists of a
sentiment and target on which opinion has been
expressed. For instance, in a product review
sentence, it identifies product features that have
been commented on by the reviewer and
determines whether the comments are positive or
negative. For example, in the sentence, “The
battery life of this camera is too short,” the
comment is on “battery life” of the camera
object(target) and the opinion is negative. Many
real life applications require this level of detailed
analysis because in order to make product
improvements one needs to know what
components and/or features of the product are
liked and disliked by consumers. Such information
is not discovered by sentiment and subjectivity
classification[1].
The sentiment expressed can be direct opinions or
Comparative opinion on entities. Comparisons are
related to but are also quite different from direct
opinions. For example, a typical direct opinion
sentence is “the picture quality of Camera X is
great”, while a typical comparative sentence is
“the picture quality of Camera X is better than that
of Camera Y.” We can see that comparisons use
different language constructs from direct opinions.
A comparison typically expresses a comparative
opinion on two or more entities with regard to their
shared features or attributes, e.g., “picture quality”.
A comparative opinion is sextuples of the
form: (E1, E2, A, PE, h, t), where E1 and E2 are
the entity sets being compared based on their
shared aspects A (entities in E1 appear before
entities in E2 in the sentence), PE({E1, E2}) is the
preferred entity set of the opinion holder h, and t is
the time when the comparative opinion is
expressed[6]. A comparative sentence expresses a
relation based on similarities or differences of
more than one entity. There are several types of
comparisons. They can be grouped into two main
categories: gradable comparison and non-gradable
comparison. The types of gradable comparatives
are 1) Non-equal gradable that express a total
ordering i.e greater than or less than of some
entities with regard to their shared features. 2)
Equative express whether two objects as equal
with respect to some features. 3) Superlative ranks
one object over all others. Non-gradable do not
explicitly grade the sentences which compare
features of two or more entities[3]. A typical
comparative sentence is “the picture quality of
Camera X is better than that of Camera Y.” We
can see that comparisons use different language
constructs from direct opinions. A comparison
typically expresses a comparative opinion on two
or more entities with regard to their shared features
or attributes, e.g., “picture quality”.
The opinion can be explicit or implicit within the
document; it is easier to detect an explicit opinion.
For example “Coke tastes good” is an explicit
opinion while “I bought the mattress a week ago,
and a valley has formed” gives implicit opinion.
The rest of the paper is organized as follows:
Section 3 we describe the opinion mining process.
Section 4 is application of opinion mining and
sentiment analysis, section 5 we discuss evaluation
of result. Section 6 is focused on research
challenges in this field and lastly is conclusion.
3. OPINION MINING PROCESS
The opinion mining process is explained in the fiq
1below. The raw data is collected from various
social media ,we can also write a crawler to extract
the data from it. The Data set available online for
research work is listed below.
Hotel review data set Trip Advisor
(http://sifaka.cs.uiuc.edu/~wang296/LARA/TripAd
visor)
MP3 review data set from Amazon
(http://sifaka.cs.uiuc.edu/~wang296/LARA/Amazo
n/mp3)
Review data set from Amazon
(http://liu.cs.uic.edu/download/data)
Review data set from
Epinions(http://www.sfu.ca/~sam39/Datasets/Epini
onsReviews
After data collection we preprocess the data
to have a structured set of reviews so that we can
apply classification techniques to classify the
opinion either as positive, negative or neutral. In
this paper we have not focused on lexicon-based
approach in which we use dictionaries of words
annotated with the word‟s semantic orientation, or
polarity. The paper is mainly focused on text
classification approach which involves building
classifiers from labeled instances of texts or
sentences [5], essentially a supervised
classification task.
Sangeeta Oswal et al, Int.J.Computer Technology & Applications,Vol 4 (4),660-665
IJCTA | July-August 2013 Available [email protected]
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ISSSN:2229-6093
The supervised classification is discussed in detail
with the algorithm like SVM, Naïve Bayes and
Nearest neighbor .
3.1Supervisedclassification:
Sentiment classification is usually formulated as a
two-class classification problem, positive and
negative. Training and testing data used are
normally product reviews. Any existing supervised
learning method can be applied, e.g., naïve Bayes
classification, and support vector machines (SVM)
(Joachims, 1999; Shawe-Taylor and Cristianini,
2000). Pang, Lee and Vaithyanathan (2002) was
the first paper to take this approach to classify
movie reviews into two classes, positive and
negative.
Figure 1: Opinion Mining Process
Fiqure 1:Opinion Mining Process
ooo
Sentiment classification
Neutral Positive Negative
Sentiment Application
Forecasting
Prediction
Business Intelligence
Decision Making
Text classification approach
Supervised
Classification Unsupervised
classification
n- gram TF-idf POS PMI
Unstructured
raw text
Data Preprocessing
blogs forum discussion Reviews
Sangeeta Oswal et al, Int.J.Computer Technology & Applications,Vol 4 (4),660-665
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ISSSN:2229-6093
Feature Extraction: It is the process where
properties are extracted from the data, because the
whole input data is too large to use in classification
N-gram model: The n-gram is a contiguous
sequence of n items from a textual or spoken
source In case of unigram (n=1), each text is a
document and is spilt up into words. The term
frequency (tf(w,d)) is the number of times that a
word w occurs in a document d.
…. (eq.1)
The term presence tp(w,d) only checks if a word w
is present within a document d which result in
binary value.
d
…. (eq.2)
However frequent words are not necessarily good
feature for classification. If the distribution of a
highly frequent world is uniformly distributed over
the classes, then the discriminative power is low.
In case of n=2(big rams) the items consist of two
consecutive words i.e the set contains all
combination of two words that are consecutive in
the original text e.g “This car is good” can be split
into bigram as „this car‟ ,‟car is‟ ,‟is good‟.
The same is true for n=3(trigram) and higher value
of n.
Tf-idf Measure: The tf-idf (term frequency-inverse
document frequency) measure is a statistic that
reflect the importance of a word across a set of
document. The term frequency and document
frequency are stated above in eq.1 and eq.2.The
inverse document frequency is used to measure the
rareness of a word across all the document. Higher
the value of inverse document frequency rare the
word across the set of document.
The inverse document frequency idf(w,D) of the
word w across all document D is shown as
…. (eq.3)
However it is a question whether the tf-idf is a
good measure f or feature selection.The tf-idf value
is high , when a word occur often in a document
and does not occur often within all document.
Part-of-speech Tagger: The POS tagger, is a
method of marking up a word in a text
corresponding to a particular part-of-speech. The
idea behind this is that only a limited set of word in
a sentence indicate the sentiment, referred to as
sentiment-words. In English language POS
examples are noun, verb, adverb and adjective.
The pos tagging technique is often used, which is
applied in several papers [5,10]
3.2 Classification Algorithm :
Classification algorithm like nearest neighbour,
naive Bayes, maximum entropy and Support vector
machine (SVM) are applied in many domains.
Nearest Neighbour:
It is applied in domain where the number of
dimension is low. It is known from the literature
[3]that nearest neighbour has more difficulties in
higher dimension space.
Naïve Bayes
The naïve bayes algorithm uses Bayes‟
theorem .The formula P(C|F) states the conditional
probability of C given F, where C is a class label
and F a feature .
…. (eq.4)
It allows calculating unknown conditional
probability form a known conditional probability
together with the prior probabilities‟. It is assumed
that the presence of a feature is unrelated to the
presence of any other feature.
The major advantage of naïve bayes models is the
fact that a relatively small training set is sufficient
to train the model. It is a good model to use as
reference for testing the quality of other models.
The naïve bayes classifier has been applied in quite
a lot of papers about sentiment analysis.
Support Vector Machine: SVM exit in different
form linear and non-linear. It is a supervised
classifier. In ideal situation the classes are linearly
….
Sangeeta Oswal et al, Int.J.Computer Technology & Applications,Vol 4 (4),660-665
IJCTA | July-August 2013 Available [email protected]
663
ISSSN:2229-6093
separable. In such situation a line can be found
which spilt the two classes perfectly.
In practice the classes are usually not linearly
separable; in such cases a higher order function can
spilt the dataset. A function is applied to the data
set which maps the point in the non linear data set
to point in a linear data set.
Although it is possible to create a model that
perfectly separates the data, it is not desirable
because such models are over fitting on the
training data.
SVM classifier are applied in many papers [5, 8, 9,
10].they are very popular in recent research . The
popularity due to the good overall empirical
performance.
Comparing the naïve Bayes and SVM
classifier ,the SVM has been applied the most
3.3 Unsupervised classification
Point mutual information (PMI) is a simple
association, which can be used for unsupervised
learning. The classification is based on the average
semantic orientation, which make use of reference
words, for most positive and most negative
association.
…. (eq.5)
This PMI measure is used in a sentiment
orientation function SO ,it formalize the
dependence of the positive and dependence of the
negative sentiment.
…. (eq.6)
This semantic orientation function can be applied
to all the extracted words in an message.
Averaging all those sentiment orientation values of
a message result in a quality. The number can be
interpreted as a sentiment according to the
positivity.
The area of unsupervised classification algorithm
is somewhat underdeveloped compared to
supervised classification algorithm .
Another unsupervised approach is the lexicon-
based method, which uses a dictionary of
sentiment words and phrases with their associated
orientations and strength, and incorporates
intensification and negation to compute a
sentiment score for each document (Taboada et al.,
2011). This method was originally used in sentence
and aspect-level sentiment classification (Ding, Liu
and Yu, 2008; Hu and Liu, 2004; Kim and Hovy,
2004).
4. Application of opinion mining
Some of the applications of sentiment analysis
includes online advertising, hotspot detection in
forums , web blog author‟s attitude analysis,
sentiment filtering etc.
Opinion mining for recommendation system:
One possibility is as an augmentation to
recommendation systems since it might not
recommend items that receive a lot of negative
feedback.
Opinion mining for Ad Placment
In online systems that display ads as sidebars, it is
helpful to detect WebPages that contain sensitive
content inappropriate for ads placement; for more
sophisticated systems, it could be useful to bring
up product ads when relevant positive sentiments
are detected, and perhaps more importantly, nix the
ads when relevant negative statements are
discovered.
Opinion mining in Business Intelligence
When faced with tremendous amount of online
information , information seekers usually find it
very difficult to yield accurate information that is
useful to them, which has motivated the research in
hotspot detection.
Sentiment analysis find a major role in Business
Intelligence to extract and visualize comparative
relation between customer review and help
enterprise discover potential risk and future design
of new product and marketing strategies.
Using an Opinion Mining Approach we can
Exploit Web Content in Order to Improve
Customer Relationship Management and provide
better service to the customer by improving on the
product quality and making the product
personalized according to the customer view point.
Sangeeta Oswal et al, Int.J.Computer Technology & Applications,Vol 4 (4),660-665
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Opinion mining for trend prediction
Organization could perform trend prediction in
sales using Opinion mining by tracking public
viewpoints.
In stock market we can analysis the sentiment
related to detect whether the stock price will be
higher or lower and help the investor to take
decision related to buying or selling the stock.
Opinion mining for political domain
Opinions matter a great deal in politics. Sentiment
analysis has specifically been proposed as a key
enabling technology, allowing the automatic
analysis of the opinions that people submit about
pending policy or government-regulation,
understanding what the voters is thinking,
predicting the outcome of elections etc.
5.Evaluation :
The performance of different methods used for
opinion mining is evaluated by calculating various
metrics like precision, recall and F-measure.
Precision is the fraction of retrieved instances that
are relevant while recall is the fraction of relevant
instances that are retrieved
6.RESEARCH CHALLENGES FOR OPINION
MINING
The challenge is to detect sentiment in spoken and
written language which is easy for humans to
understand but difficult for computers to detect.
Opinions are far harder than facts to describe as
they are short and informally written and highly
diverse. The raw text contains wrong spellings,
sarcasm, idiom, abbreviations, poor grammar.
Using computer the sentiment can be analyzed for
huge data in less time but accuracy is important.
Few opinion mining challenges are listed below:
1. Analyzing natural language is difficult
enough. Sarcasm or other forms of derisive
language are extremely problematic for
technologies to interpret.
2. To identify what the person actually talking
about.
3. The problem of resolving what a phrase
refers to example "We watched the movie
and went to dinner; it was awful." What
does "It" refer to?
4. Difficulty in parsing the sentence to find
the subject and object to which verb and/or
adjective refer to.
5. The opinion on twitter have abbreviations,
lack of capitals, poor spelling, poor
punctuation, poor grammar and so difficult
to understand.
6. The detection of spam and fake reviews,
mainly through the identification of
duplicates, the comparison of qualitative
with summary reviews, the detection of
outliers, and the reputation of the reviewer.
7. Language which is another challenge, most
of the work done in sentiment analysis is
focused on English and Chiness language
other languages are yet to be explore.
8. In one context the statement can be positive
and in other it can be negative. For example,
“fighting” is negative in a war context but
positive in a medical one. Different
sentiment for different domains.
9. A single word can be used to convey three
different opinions, positive, neutral and
negative respectively depending on its use
and context.
7 .Conclusion : Sentiment analysis can be applied
to a wide domain to classifying and summarizing
review and prediction. However, finding opinion
sources and monitoring them on the Web can still
be a difficult task because there are a large number
of diverse sources, and each source may also have
a huge volume of opinionated text (text with
opinions or sentiments).Also the fact that
sentiment analysis is a natural language processing
task, which is not an easy problems.
Sangeeta Oswal et al, Int.J.Computer Technology & Applications,Vol 4 (4),660-665
IJCTA | July-August 2013 Available [email protected]
665
ISSSN:2229-6093
Due to its tremendous value for practical
applications, there has been an explosive growth of
both research in academia and applications in the
industry. In this paper the opinion mining is
explained covering its process, application and
challenges. In future, more work is needed on
further improving the performance measures.
Sentiment analysis can be applied for new
applications like depression analysis, sentiment
analysis from songs etc.
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IJCTA | July-August 2013 Available [email protected]
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ISSSN:2229-6093