bi journal bi sentimental analytics[1]
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41BUSINESS INTELLIGENCEJOURNAL VOL. 15, NO. 2
SENTIMENT ANALYSIS
BI and SentimentAnalysis
Mukund Deshpande and Avik Sarkar
Overview
Over the past two decades, there has been explosive growth
in the volume of information and articles published on the
Internet. With this enormous increase in online content
came the challenge of quickly finding specific information.
Google, AltaVista, MSN, Yahoo, and other search sites
stepped in and developed novel technologies to efficientlysearch and harness the massive amount of Internet
information. Some search engines indexed keywords; others
used information hierarchies, arranging Web pages in a
structured way for easy browsing and for quickly locating
requested information. Text classification, also known as
text categorization, and text-clustering-based techniques
advanced, allowing Web pages to be automatically
organized into relevant hierarchies.
Web sites frequently discuss consumer products or
servicesfrom movies and restaurants to hotels andpolitics. ese shared opinions, termed the voice of the
customer, have become highly valuable to businesses and
organizations large and small. In fact, a recent study by
Deloitte found that 82 percent of purchase decisions have
been direct ly influenced by reviews. e rapid spread of
information over the Internet and the heightened impact
of the media have broken down physical and geographical
boundaries and caused organizations to become increas-
ingly cautious about their reputations.
Businesses and market research firms have carried outtraditional sentiment analysis (also referred to as opinion
analysis or reputation analysis) for some time, but it
requires significant resources (travel to a given location;
staffing the survey process; offering survey respondents
incentives; and collecting, aggregating, and analyzing
results). Such analysis is cumbersome, time-consuming,
and costly.
Dr. Mukund Deshpande is senior architect atthe business intelligence competency center of
Persistent Systems. He has helped enterprises,
e-commerce companies, and ISVs make better
business decisions for the past 10 years by using
machine learning and data mining techniques.
Dr. Avik Sarkar is technical lead at the analytics
competency center at Persistent Systems and
has over nine years of experience using analyt ics,
data mining, and statistical modeling techniques
across different industry vertical markets.
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Automated sentiment analysis based on text mining
techniques offers a simpler, more cost-effective solution
by providing timely and focused analysis of huge, ever-increasing volumes of content. e concept of automated
sentiment analysis is gaining prominence as companies
seek to provide better products and services to capture
market share and increase revenues, especially in a chal-
lenging global economy. Understanding market trends and
buzz enables enterprises to better target their campaigns
and determine the degree to which sentiment is positive,
negative, or neutral for a given market segment.
Text Mining
Research and business communities are using textmining to harness large amounts of unstructured textual
information and transform it into structured information.
Text mining refers to a collection of techniques and
algorithms from multiple domains, such as data mining,
artificial intelligence, natural language processing (NLP),
machine learning, statistics, linguistics, and computational
linguistics. e objective of text mining is to put the
already accumulated data to better use and enhance an
organizations profitability. With a variety of customer
trends and behavior and increasing competition in each
market segment, the better the quality of the intelligence,the better the chances of increasing profitability.
e major text mining techniques include:
Text clustering:e automated grouping of textual
documents based on their similarityfor example,
clustering documents in an enterprise to understand its
broad areas of focus
Text classification or categorization:e automated
assignment of documents into some specific topicsor categoriesfor example, assigning topics such as
politics, sports, or business to an incoming stream of
news articles
Entity extraction:e automated tagging or extraction
of entities from textfor example, extracting names of
people, organizations, or locations
Document summarization:An automated technique
for deriving a short summary of a longer text document
Sentiment analysis applies these techniques to assign
sentiment or opinion information to certain entities within
text. Sentiment evaluation is another step in the process of
converting unstructured content into structured content
so that data can be tracked and analyzed to identify trends
and patterns.
Sentiment Analysis
Sentiment analysis broadly refers to the identification and
assessment of opinions, emotions, and evaluations, which,
for the purposes of computation, might be defined aswritten expressions of subjective mental states.
For example, consider this unstructured English sentence
in the context of a digital camera review:
Canon PowerShot A540 had good aperture
combined with excel lent resolution.
Consider how sentiment analysis breaks down the informa-
tion. First, the entities of interest are extracted from the
sentence:
Digital camera model: Canon PowerShot A540
Camera dimensions or features: aperture, resolution
Sentiments are further extracted and associated for each
entity, as follows:
Digital camera model = Canon PowerShot A540;
Dimension = aperture, Sentiment = good (positive)
Digital camera model = Canon PowerShot A540;
Dimension = resolution, Sentiment = excel lent (positive)
Based on the individual sentence-level sentiments,
aggregated and summarized sentiment about the digital
camera is obtained and stored in the database for
reporting purposes.
SENTIMENT ANALYSIS
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e following sections delve into the technical details andalgorithms used for this type of sentiment analysis.
Sentiment Analysis Steps
Suppose we are interested in deriving the sentiment or
opinion of various digital cameras across dimensions such
as price, usability, and features. Figure 1 illustrates the steps
we will follow in this analysis.
Step 1: Fetch, Crawl, and Cleanse
Comments about digital cameras might be available on
gadget review sites or in discussion forums about digitalcameras, as well as in specialized blogs. Data from all of
these sources needs to be collected to give a holistic view
of all the ongoing discussions about digital cameras. Web
crawlerssimple applications that grab the content of a
Web page and store it on a local diskfetch data from the
targeted sites. e downloaded Web pages are in HTML
format, so they need to be cleansed to retain only the
textual content and the remaining HTML tags used for
rendering the page on the Web site.
Step 2: Text Classificatione sites from which data is fetched might contain extra
information and discussions about other electronic gadgets,
but our current interest is limited to digital cameras. A text
classifier determines whether the page or discussions on it
are related to digital cameras; based on the decision of the
classifier, the page is either retained for further analysis or
discarded from the system.
e text cla ssifier is provided by a list of relevant (positive)and irrelevant (negative) words. is list consists of a base
list of words supplied by the software provider, which is
typically enhanced by the user (the enterprise) to make
it relevant to the particular domain. A simple rule-based
classifier determines the polarity of the page based on the
proportion of positive or negative words it contains. You
can train complex and robust classifiers by feeding them
samples of positive and negative pages. ese samples
allow you to build probabilistic models based on machine-
learning principles. en, these models are applied on
unknown pages to determine the pages relevance.
Commercial forums, blog aggregation services, and search
engines (such as BoardReader and Moreover) have become
popular recently, eliminating the need to build in-house
text classifiers. You can use these services to specify
keywords or a taxonomy of interest (in this case, digital
camera models), and they will fetch the matching forums
or blog articles.
Step 3: Entity Extraction
Entity extraction involves extracting the entities from thearticles or discussions. In this example, the most important
entity is the name or model of the digital cameraif the
name is incorrectly extracted, the entire sentiment or
opinion analysis becomes irrelevant. ere are three major
approaches for entity extraction:
Dictionary or taxonomy:A dictionary or taxonomy
of available and known models of digital cameras is
provided to the system. Whenever the system finds
SENTIMENT ANALYSIS
Figure 1.Sentiment analysis steps
Fetch/Crawl
+ Cleanse
Text
Classification
Entity
Extraction
Sentiment
Extraction
Sentiment
Summary
Reports/
Charts
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a name in the article, it tags it as a digital camera
entity. is technique, though simple to set up, needs
frequent updates on every subsequent model launch,so its not robust.
Rules:A digital camera model name has a certain
pattern, such as Canon PowerShot A540. erefore,
a rule may be written to tag any alphanumeric token
following the string Canon PowerShot as a digital
camera model. Such techniques are more robust than
the dictionary-based method, but if Canon decides to
launch a new model, say the SuperShot, such rules must
be updated manually.
Machine learning:is algorithm learns the extraction
rules automatically based on a sample of articles with
the entities properly tagged. e rules are learned by
forming graphical and probabilistic models of the
entities and the arrangement of other terms adjoining
them. Popular machine learning models for entity
extraction are based on hidden Markov models (HMM)
and conditional random fields (CRF).
Step 4: Sentiment Extraction
Sentiment extraction involves spotting sentiment wordswithin a particular sentence. is is typically achieved
using a dictionary of sentiment terms and their their
semantic orientations. ere are obvious limitations to the
dictionary-based approach. For example, the sentiment
word high in the context of price might have a negative
polarity, whereas high in the context of camera resolu-
tion will be of positive polarity. (Approaches to dealing
with varying and domain-specific sentiment words and
their semantic orientation are discussed in the next section.)
Once an entity of interest (for example, the digital cameramodel or sentiment word) is identified, structured senti-
ment is extracted from the sentence in the form of {model
name, score}, where score is the positive or negative polarity
value of the identified sentiment word in the sentence. If
some dimension (such as price or resolution) is also
found in the sentence, then the sentiment is extracted in
the form of {model name, dimension, score}. We may also
choose to report the source name or source ID to associate
the extracted sentiment back to that source.
e presence of negation words, such as not, no,
didnt, and never, require special attention. ese
keywords lead to a transformation in the polarity valueof the sentiment words and hence in their reported score.
Natural-language techniques are used to detect the effect
of the negation word on the adjoining sentiment word.
If the negation effect is detected, then the polarity of the
sentiment word is inverted.
e extracted sentiment data is now in a structured format
that can be loaded into relational databases for further
transformation and reporting.
Step 5: Sentiment Summarye raw sentiments extracted in Step 4 come from
individual sentences that are specific to certain entities.
To make the data meaningful for reporting, it must
be aggregated. One of the obvious aggregations in the
context of digital cameras will be model-name-based
aggregationin this case, all of the positive, negative, or
neutral entries in the database are grouped together. Again,
model- and dimension-based sentiment aggregation would
allow the discovery of detailed, dimension-wise sentiment
distribution for every model. Based on the reporting needs,
different levels of aggregation and summarization need tobe carried out and stored in a database or data warehouse.
Step 6: Reports/Charts
Reports and charts can be generated directly from the
database or data warehouse where the aggregated data is
stored in a structured format. Such reporting falls under
the purview of traditional BI and reporting, and is not
related to the core sentiment analysis steps.
e steps described above have been used to transform
the unstructured textual data in blogs and forums tostructured, quantifiable numeric sentiment data related to
the entity of interest.
Sentiment Analysis Challenges
ere are challenges in sentiment analysis, but fortunately
some simple tactics can help you overcome them. e
challenges discussed in this section are related to sentiment
assignment, co-reference resolution, and assigning domain-
specific polarity values to sentiment words.
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Sentiment Assignment
Suppose a sentence mentions digital camera features such
as resolution, usage, and megapixels; the sentence alsomentions a sentiment word, say, good. Should we relate
all or only some of the features to the sentiment word?
e issue becomes even more challenging when multiple
sentiment words or model names are mentioned in the
same sentence. Limited accuracy can be achieved by using
simple heuristics, such as assigning the model name or
feature to the nearest occurring sentiment word (this yields
acceptable accuracy). Deep NLP techniques may be used
to identify the model names or features (nouns) that are
related to the sentiment word (adjective or adverb) in thecontext of that sentence.
Reviews often include comparative comments about
multiple digital camera models within single sentences. For
example:
Kodak V570 is better than the Canon
Power-Shot A460.
Kodak V570 scores more points than Canon
PowerShot A460 in terms of resolution.
In comparing the Kodak V570 and Canon Power-
Shot A460, the latter wins in terms of resolution.
Nikon D200 is good in terms of resolution, while
Kodak V570 and Canon PowerShot A460 have
better usability.
Dealing with such comparative sentences requires building
complex natural-language rules to understand the impact
and span of every word. For example, the word betterwould signal a positive sentiment extraction for one camera
model or feature and negative sentiment data for another.
Co-reference Resolution
Suppose a discussion about a digital camera mentions
the model in the beginning of the article, but subsequent
references use pronouns such as it or phrases such as the
camera. Referring to a proper noun by using a pronoun is
called co-reference.
Co-reference is a common feature of the English language.
Ignoring sentences that use it will lead to a loss in data and
incorrect reporting. Co-reference resolution, also referred toas anaphora resolution, is a vast area of research in the NLP
and computational linguistics communities. It is achieved
using rule-based methods or machine-learning-based
techniques. Open source co-reference resolution systems
such as GATE (General Architecture for Text Engineering)
provide the accuracy required for sentiment analysis.
Domain-specific polarity values and sentiment words
As discussed earlier, sentiment words have different
interpretations in different contexts. For example, long
in the context of movies might convey a negative senti-ment, whereas in sports it would indicate positive polarity.
Similarly, unpredictable might convey positive sentiment
for movies, but would indicate negative polarity when used
to describe digital cameras or mobile phones.
is problem can be tackled by using a domain-specific
sentiment word list. Such a list is created by analyzing all
the adjectives, adverbs, and phrases in the domain-specific
document collection. e analysis calculates the proximity
of these words to generic positive words such as good and
generic negative words such as bad. Another calculationis called point-wise mutual information, which provides a
measure of whether two terms are related and hence jointly
occurring, rather than showing up together by chance.
ese calculations can be performed for the word across all
documents to determine whether a word occurs more often
in the positive sense than in the negative sense.
ese techniques work well if a certain sentiment word has
a fixed polarity interpretation within a certain domain.
Now, suppose we have the sentiment word high, which
in the digital camera domain could indicate negativesentiment for price but positive sentiment for camera
resolution. Such cases are a bit more difficult to handle
and can often lead to errors in sentiment analysis. To tackle
such scenarios, the system has to store some mapping of
entity, the sentiment word, and its associated pola rityfor
example, {high, price, ve} and {high, resolution, +ve}.
Creating and verifying such mappings involves considerable
manual work on top of automated techniques.
SENTIMENT ANALYSIS
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Examples
Sentiment Analysis of Digital Camera Reviews
ere are many Web sites that contain reviews related todigital cameras. Suppose a consumer is looking to buy a
particular digital camera and would like to get a complete
understanding of the cameras different features, strengths,
and weakness. She would then compare this information
to other contemporary digital camera models of the same
or competing brands. is would involve manual research
across all related Web sites, which might require days
or even months of research. Rather than doing this, the
consumer is more likely to gather incomplete information
by visiting just a few sites.
Automated sentiment analysis and BI-based reporting can
come to the rescue by providing a complete overview of
the many discussions about digital camera models and
their features.
First, a list of available digital camera models is collected
from the various companies catalogs to create a compre-
hensive taxonomy of digital camera models. An initial list
of digital camera features or dimensions is also collected
from these catalogs. All online discussion pages are
collected from the digital camera review Web sites.
One important consideration during taxonomy creation is
the grouping of synonymous entities. For example, Canon
PowerShot A540 may a lso be referred to as PowerShot
540 or Canon A540. All of these should be grouped as a
single entity. Again, the dimension camera resolution may
be referred to as resolution, megapixel, or simply MP;
all should be aligned to the single entity resolution.
e presence of the camera model name on a given page
indicates that it should be considered for further analysis.e next challenge is to extract the entities of interest from
the textthat is, the digital camera model names and
features. A taxonomy-based method is used to extract those
that are known. Machine-learning-based approaches can
extract the others. Here, documents tagged with existing
model names and features are provided as training to the
machine learning the algorithm, which uses the data to
learn the extraction rules. ese rules are then used to
extract entities from other incoming articles.
Raw sentiment is extracted from the sentiment-bearing
sentences using the approaches described above. A list
of sentiment-bearing words, along with their polarityvalues, is provided as input. Based on the raw sentiments,
sentiment aggregation is carried out on two dimensions:
digital camera model and digital camera features. Further
aggregation can be carried out for each Web site to identify
any site-specific bias in the extracted sentiments. ese
aggregated values are then stored in the data warehouse for
reporting purposes.
Sentiment Analysis of Election Campaigns
e most recent U.S. presidential election saw a la rge
number of online Web sites discussing the post-electionpolicies and agendas of Democratic nominee Barack
Obama and Republican John McCain. ese discussions
come from people who are very likely to be legitimate
American voters (rather than, say, chi ldren or people resid-
ing outside the U.S.). Political parties such as Democrats
and Republicans employ armies of people across the U.S.
to survey people about their opinions on the policies of the
presidential candidates. ese surveys incur huge costs and
delays in information collection and analysis.
Automated BI and sentiment ana lysis can work magic hereby continuously analyzing the comments posted on Web
sites and providing prompt, sentiment-based reporting.
For example, a popular presidential debate on television
one evening will lead to comments on the Web. Sentiment
analysis performed on the comments can be completed in
real time, and the political parties can gauge the response
to the debate and to the policy matters discussed. Smart
technology use and intelligent data collection can provide
in-depth, state-wide sentiment analysis of the comments.
Such analysis would be extremely powerful in determining
the future election campaigning strategy in each state.
Considering the sensitivity and impact of the analysis,
careful attention must be paid to generating the taxonomy,
which consists of two main entities: the presidential nomi-
nees and the policies or issues discussed. e presidential
nominees list is finite, corresponding to the major political
parties. Variations in the names, acronyms, or synonyms
should also be carefully studied and collated.
SENTIMENT ANALYSIS
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Generating the taxonomy of issues or policies is far more
challenging. Each issue is defined in terms of keywords
or phrases; some of these will appear in multiple issues orpolicies. Variations among keywords and phrases can be
quite large, and capturing them requires considerable time
and effort. Automated methods may be used for many of
these steps, but manual verification and editing is required
to remove discrepancies. Another challenge is determining
the location of each person entering comments. is can
be done by capturing their Internet protocol (IP) addresses,
then associating them with physical and geographical
locations. Comments from outside the country are ignored.
Other comments are associated with states (or cities, as
available). Finally, carefully selected, election-specificsentiment words are added to the ta xonomy.
Once the taxonomy is in place, the raw sentiments
may be extracted from the comments. ey are in two
primary forms:
{Presidential Nominee, Location, Sentiment}, which
captures generic sentiment about the presidential
candidate regardless of issue
{Presidential Nominee, Issue or Policy, Location,
Sentiment}, which captures the sentiment or opinion
about the particular issue for the presidential candidate
A sing le comment may lead to the extraction of more
than one raw sentiment, as shown above. Next, the data is
aggregated along dimensions such as presidential nominee,
policy issue, or location. e aggregated results are stored
in a warehouse for quick access and reporting.
In the future, many Web sites will likely collect further
details about the people making the comments, including
age group, income, education, religion, race, ethnic origin,
and number of family members. is would allow more
detailed analysis and drill-down of the sentiment results,
which would aid in advanced campaign management such
as micro-targeting specific groups of voters.
SENTIMENT ANALYSIS
Figure 2.Sample election campaignvoter sentiment report
Washington
Oregon
ArizonaNew Mexico
Texas
Kansas
Colorado
Utah
Nevada
California
Idaho
Montana North Dakota
South Dakota
Nebraska
Minnesota
Iowa
Missouri
Arkansas
Mississippi Alabama
Louisiana
Florida
Georgia
Tennessee
Wisconsin
IllinoisIndiana
Ohio
Michigan
Kentucky
Oklahoma
New YorkR.I.
Mass.
N.H.
Maine
Wyoming
Pennsylvania
Virginia
VirginiaWest
Md.
Vt.
North Carolina
South
Alaska
Negative
Obama McCain
Positive
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SENTIMENT ANALYSIS
Other Applications of BI and Sentiment Analysis
Additional applications of sentiment ana lysis and BI-based
reporting include:
Online product reviews.ese contributed to the
development of sentiment analysis. Product reviews are
analyzed to provide an overall idea about the features of
the product along with its strengths and weaknesses.
Online movie reviews.Tese are available in
abundance, which led to the discovery of a new
domain of sentiment analysis that analyzes peoples
opinions about movies.
Company news.Analyzing news articles and
discussions related to a company can provide
detailed sentiment analysis about an organizations
performance, along with criteria such as profit,
customer satisfaction, and products.
Online videos.Sentiment analysis helps to capture
opinions about both video quality and the
events portrayed.
Hotels, vacation homes, holiday destinations,and restaurants.Sentiment analysis helps people
make informed decisions about holiday plans or
where to dine out.
Movie stars, popular sports figures, and televi-
sion personalities.Sentiment analysis can capture
the sentiments and opinions of large groups of
people by analyzing discussions or articles related to
such public figures.
Existing Research in Sentiment AnalysisSentiment/opinion analysis is an emerging area of research
in text mining. Early researchers rated movie reviews on
a positive/negative scale by treating each review as a bag
of words and applying machine-learning algorithms like
Nave Bayes. Successive research progressed to detecting
sentence-level sentiment and hence reporting higher
accuracy figures. In contrast to the research on movie
reviews, experts from the finance domain analyzed the
sentiment in published news articles to predict the price of
a certain stock for the following day.
Experts also discovered new techniques for using Web
search to determine the semantic orientation of words,
which is at the core of quantifying the sentiment expressed
in a sentence. See the bibliography at the end of this article
for additional studies and reports.
Final Thoughts
In closing, we would like to spotlight two observations that
highlight the growing need for sentiment analysis:
With the explosion of Web 2.0 platforms such asblogs, discussion forums, peer-to-peer networks,
and various other types of social media all of
which continue to proliferate across the Internet
at lightning speed, consumers have at their
disposal a soapbox of unprecedented reach and
power by which to share their brand experiences
and opinions, positive or negative, regarding
any product or service. As major companies are
increasingly coming to realize, these consumer
voices can wield enormous influence in shap-
ing the opinions of other consumersand,ultimately, their brand loyalties, their purchase
decisions, and their own brand advocacy. Com-
panies can respond to the consumer insights they
generate through social media monitoring and
analysis by modifying their marketing messages,
brand positioning, product development, and
other activities accordingly.
Jeff Zabin and Alex Jefferies [2008]. Social Media
Monitoring and Analysis: Generating Consumer Insights from
Online Conversation, Aberdeen Group Benchmark Report.
Marketers have always needed to monitor media
for information related to their brandswhether
its for public relations activities, fraud violations,
or competitive intelligence. But fragmenting
media and changing consumer behavior have
crippled traditional monitoring methods.
Technorati estimates that 75,000 new blogs are
created daily, along with 1.2 million new posts
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SENTIMENT ANALYSIS
each day, many discussing consumer opinions on
products and services. Tactics [of the traditional
sort] such as clipping services, field agents, and adhoc research simply cant keep pace.
Peter Kim [2006]. Te Forrester Wave: Brand Monitor-
ing, Q3 2006, white paper, Forrester Wave.
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Framework and Graphical Development Environment
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Das, Sanjiv Ranjan, and Mike Y. Chen [2001]. Yahoo!
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Esuli, Andrea, and Fabrizio Sebastiani [2006].
SentiWordNet: A Publicly Available Lexical Resource
for Opinion Mining. Proceedings of LREC-06, 5th
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Hurst, Matthew, and Nigam Kamal [2004]. Retrieving
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Lafferty, John, Andrew McCallum, and Fernando Pereira
[2001]. Conditional Random Fields: Probabilistic
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Nigam, Kamal, and Matthew Hurst [2004]. owards a
Robust Metric of Opinion. AAAI Spring Symposium on
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[2004].A Sentimental Education: Sentiment
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Submission Deadline: December 17, 2010
Distribution Date: March 2011
Editorial Calendar andInstructions for Authors