Sentiment analysis aims to determine the subjectivity and polarity of texts. It involves detecting objectivity vs subjectivity, extracting opinions, and classifying polarity. Approaches include machine learning and lexical methods using resources like SentiWordNet. Challenges include handling negations, thwarted expectations, and domain transferability. SentiWordNet assigns polarity scores to WordNet synsets. Researchers have used these scores in various ways to classify text polarity, such as averaging scores or comparing positive vs negative word counts. Summarization may improve accuracy, but domain differences pose issues for cross-domain classification.

1. SENTIMENT ANALYSIS
SENTIWORDNET AND POLARITY
CLASSIFICATION
Roseline Antai

2. Sentiment Analysis
 Introduction
 Objectivity and Subjectivity detection
 Opinion extraction
 Polarity classification
 Approaches to Sentiment Analysis
 Issues in Sentiment Analysis
 SentiWordNet
 My Work
 References

3. Sentiment Analysis-Introduction
Sentiment Analysis- “What‟s the Point?”

4. Introduction
 People these days place so much value on what
others think, and this shapes their decisions.
 In everyday life, when it comes to making
decisions, people yearn to know the feelings, and
the experiences of others, before making up their
minds.
 People also consult political discussion forums to
aid in making up their minds on the votes to cast,
read consumer reports before buying appliances,
ask the opinions of friends before going to a
certain restaurant.
 The World Wide Web has now created an avenue
for the opinions of others to be expressed freely,
and accessed by others.

5. Intro...
 Sentiment analysis has been referred to as:
 Subjectivity analysis
 Opinion mining
 Appraisal extraction
 Some connections to affective computing

6. Intro...
 Sentiment analysis is an area which tries to
determine the mindset of an author, through a
body of text.
 Sentiment analysis or statement polarity
classification has to do with determining the
relative positivity or negativity of a document, web
page or selection of text. This task is a difficult one
because of the variations and complexities that
exist in language expressions.
 Sentiment analysis has also been defined as the
task of identifying positive and negative opinions,
emotions and evaluations.

7. Intro...
 The important steps in sentiment analysis
 Objectivity/Subjectivity detection in text
 Opinion extraction
 Polarity classification

8. Objectivity and Subjectivity
detection
 While some researchers have used algorithms
for detecting subjective text, others have used
syntactic rules.
 Morinaga et al (2002), on the basis of human-
test samples generated in advance syntactic
and linguistic rules to determine if any given
statement is an opinion or not. Statements are
collected from the web about the target
products whose reputations they are working
on, and then passed over these rules, and
opinions are extracted.

9. Opinion Extraction
 Sentiment extraction from investor message boards, using five
different algorithms, (Naïve classifier, vector- distance
classifier, discriminant-based classifier, adjective-adverb classifier
and Bayesian classifiers), to classify messages into three
types, Optimistic, Pessimistic, and neutral, where the neutral
statements are the objective statements which do not fall in either
class.
 PMI-IR algorithm used to extract two consecutive words, where one
was an adjective or adverb, while the second provided context. This
is due to the fact that an adjective may have a different
orientation, depending on the review.
 An example is the adjective “unpredictable”, which in an automotive
review would have a negative orientation, if used in a phrase like
“unpredictable steering”, while in a movie review, it would have a
positive orientation if used in a phrase such as “unpredictable plot” .

10. Polarity classification
 The first step in emotion classification research is the
question, “Which emotions should be addressed?”
(Danisman and Alpcokak, 2008).
 In the lexical approach, a dictionary or lexicon of pre-
tagged words is utilized. Each present word is
compared against the dictionary. A word‟s polarity
value is added to the „total polarity score‟ if the word is
present in the dictionary. If the polarity score of a text
is positive, the text is classified as positive. Otherwise,
it is classified as negative.
 For the machine learning approach, a series of feature
vectors are chosen and a collection of tagged corpora
are provided for training a „classifier‟, and this can
then be applied to an untagged corpus of text.

11. Machine Learning Approach
 The machine learning approach utilizes
machine learning algorithms such as:
 Naïve Bayes
 Maximum entropy
 SVM/SVM Light
 ADTree (Alternating Decision Tree)
 The Lexical approach makes use of lexicons
like the GI lexicon, WordNet, ConceptNet and
SentiWordNet.

12. Application Domains
 Reviews
 Political blogs
 News Articles/Editorials
 Business message boards

13. Issues in Sentiment Analysis
 Negations
 Thwarted expectations
 Domain transferability

14. Negations
 In using the Naïve classifier in their wok, Das and
Chen (2007) handled negation by matching each
lexical entry by a corresponding counterpart with a
negation sign. Each message before it was
processed, was treated by a parsing algorithm which
negates words if the sentence context required it. As
an example, a sentence which read “this stock is not
good,”, would have the word good, replaced by
“good__n”, to simplify a negation .
 Following their example , (Pang and Lee, 2002)
added the tag „NOT_ „ to every word between a
negation word, like “not”, “isn‟t”, “didn‟t”, etc, and the
first punctuation mark following the negation word.

15. Negations
 Some researchers, simply represent the negation with another
word. They did this, by forming a new word using the negated
verb. For example, given the sentence “I don‟t enjoy it”, they
first replaced the shortened form by the full version, - “I do not
enjoy it”, and then finally, as “I do NOTenjoy it.” Hence, the
word “enjoy” is used to form a new word “NOTenjoy”, and this
way, they were able to discriminate the word “enjoy”, which
has a positive meaning, from the word “NOTenjoy”, which has
a negative meaning.
 (Denecke, 2009) deals with negation by first scanning a
text, and identifying negated terms like “Not”, ”no” and
“nothing” . If one of these negated terms is found within two
terms of an affective word, it is assumed the word‟s polarity is
effectively reversed. Hence, any positive word around a
negative word is ranked as negative, and any negative word
around a negated term is ranked as positive.

16. Thwarted Expectations
 The term thwarted expressions has been
defined as expressions which contain a
number of words having a polarity which is
opposite to the polarity of the expression itself
(Annett and Kondrack, 2008).
 Taking the review:
“”This film should be brilliant. It sounds like
a great plot, the actors are first grade, and
the supporting cast is good as well, and
Stallone is attempting to deliver a good
performance.
However, it can‟t hold up””

17. Domain transferability
 The differences which exist in product features and widely
varying domains makes the use of automatic sentiment
classification across a wide range of domains quite difficult to
achieve (Blitzer and Pereira, 2007).
 Take a scenario where developers annotate corpora for a
small number of domains, then train these corpora, and
subsequently apply them to other similar corpora.
 This raises two questions:
 one about the accuracy of the trained classifier, when the test
data‟s
distribution is significantly different from the training distribution.
 second, which notion of domain similarity should be used to
select domains to annotate, which would serve as good proxies
for other domains.

18.  Denecke (2009) also reports on classification
across domains using SentiWordNet, and
concludes from results obtained that a classifier
trained on one domain is not transferable to
another domain without a significant drop in
accuracy.
 This may be due to the linguistic characteristics of
different domains.
 Also, average SentiWordNet scores per word
class vary for different domains.
 A classifier trained on a mixture of texts of
different domains is better suited.

19. SentiWordNet
 SentiWordNet provides for each synset of
WordNet a triple of polarity scores (positivity,
negativity and objectivity) whose values sum up to
1. For example the triple 0, 1, 0 (positivity,
negativity, objectivity) is assigned to the synset of
the term bad (Denecke,2009).
 It is a lexical resource in which each synset of
WordNet is associated with three numerical
scores, „obj‟,‟neg‟ and „pos‟. Each of the scores
ranges from „0‟ to „1‟, and their sum equals „1‟
(Saggion and Funk, 2010).

20.  The score triplet is derived by combining the
results which are produced by a committee of
eight ternary classifiers, all characterised by
similar accuracy levels.
 SentiWordNet has been created automatically by
means of a combination of linguistic and statistic
classifiers. Like WordNet 2.0 from which it has
been derived, SentiWordNet consists of around
207000 word-sense pairs or 117660 synsets. It
provides entries for nouns (71%), verbs (12%),
adjectives (14%) and adverbs (3%).

21. SentiWordNet Scores
 SentiWordNet scores have been combined in
different ways to classify text into positive or
negative polarities. Two of these are:
 Denecke (2009) whose work was on testing
the suitability of polarity scores for sentiment
classification of documents in different
domains, and analyzing accuracies in cross
domain settings.

22.  Six different domains were used, four being
Amazon product reviews
(books, DVDs, electronics and kitchen
equipments), one on drugs, and one news
articles.
 The word is stemmed and looked up in
SentiWordNet.
 As many entries may exist for a word, the scores
for positivity, negativity and objectivity of the
entries are averaged.
 The ambiguity which arises from a word having
very different values from different senses is not
addressed in this work. Eg: „bad‟, which in one
sense has pos=0,neg=1 and obj=0, and in another
sense, has pos=0.625, neg = 0.125, and obj=0.25.

23.  Instead, a simpler method of calculating the
average of the scores of all senses is utilized.
 The polarity score triple is used to determine
the semantic orientation of the word.
 If the positive value is larger, the word is
positive, and same goes for the negative,
where both are equal, the word is ignored.
 An average polarity triple for the full document
is determined by summing up the polarity
score triples of all opinionated words.

24.  If the number of positive words is larger than
the number of negative words, the document is
positive. Also, same goes for negative.
 If there are equal numbers of positive, as well
as negative words, the average polarity score
is checked if the positive value is larger than
the negative, then the document is classified
positive, and vice versa.

25.  Saggion and Funk(2010) use an English data
source and an Italian data source.
 Again WSD was not carried out.
 For each entry of the word in SentiWordNet,
the number of times the word is more positive
than negative (positive>negative), the number
of times it is more negative than positive, and
the total number of entries in SentiWordNet
are computed.

26.  In each sentence, the number of words more
positive than negative is calculated, and same
goes for the more negative words.
 The sentiment score for the sentence is positive if
most words in the sentence are positive, and
negative, if there are more negative words, and
neutral otherwise.
 The paper also reports using summarization as a
pre-process before classification, and this does
lead to a statistically significant increase in
classification accuracy.

27. DEMO
http://sentiwordnet.isti.cnr.it/

28. My Work
 Generate a simple baseline system
 Incorporate WSD in my work
 Will summarization lead to better results?
 What is the document space was reduced ?
Will this lead to better results?
 How do I make it domain adaptable?

29. References
 Morinaga, S., Yamanishi, K., Tateishi, K., and Fukushima, T. (2002). Mining product reputations on the
web. Proceedings of the 8th ACM SIGKDD international Conference on Knowledge Discovery and Data
Mining.
 Blitzer, J., Dredze, M., & Pereira, F. (2007). Biographies, bollywood, boom-boxes and blenders: Domain
adaptation for sentiment classification. Annual Meeting Association For Computational Linguistics (Vol.
45, pp. 440-447).Association for Computational Linguistics. Retrieved from
http://acl.ldc.upenn.edu/P/P07/P07-1056.pdf
 Michelle Annett and GrzegorzKondrak. 2008. A comparison of sentiment analysis techniques: polarizing
movie blogs. In Proceedings of the Canadian Society for computational studies of intelligence, 21st
conference on Advances in artificial intelligence (Canadian AI'08), Sabine Bergler (Ed.). Springer-
Verlag, Berlin, Heidelberg, 25-35.
 Das, S. and M. Chen 2007. Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web.
Management Science 53(9): 1375-1388.
 Mejova,Y.(2009). Sentiment Analysis: An Overview. Computer Science department, University of Iowa.
www.cs.uiowa.edu/~ymejoya/publications/comps YelenaMejova.pdf
 Thumbs up? Sentiment Classification using Machine Learning Techniques. Bo Pang, Lillian Lee, and
ShivakumarVaithyanathan. Proceedings of the Conference on Empirical Methods in Natural Language
Processing (EMNLP), pp. 79--86, 2002.
 Turney, P 2002. Thumbs up or thumbs down?: semantic orientation applied to unsupervised
classification of reviews. In Proceedings of the 40th Annual Meeting on Association for Computational
Linguistics (ACL '02). Association for Computational Linguistics, Stroudsburg, PA, USA, 417-424.
DOI=10.3115/1073083.1073153 http://dx.doi.org/10.3115/1073083.1073153

30. References
 Danisman, T and Alpkocak, A. “Feeler: Emotion classification of text using vector space model,”
in AISB 2008 Convention, Communication, Interaction and Social Intelligence, vol. vol. 2,
Aberdeen, UK, April 2008.
 Wilson, T., Wiebe, J., and Hoffmann, P. (2009). Recognizing contextual polarity: an exploration of
features for phrase-level sentiment analysis. Computational Linguistics, 35(5):399–433.
 Denecke , K. Are SentiWordNet Scores suited fro multi-domain sentiment classification? ICDM,
2009.
 Saggion, H. and Frank, A. Interpreting SentiWordNet for opinion classification. In proceedings of
LREC, 2010.
 Esuli, A. , Baccianella, S. and Sebastiani, F. SentiWordNEt 3.0: An Enhanced Lexical Resource
for Sentiment Analysis and Opinion Mining. In proceedings of the seventh conference on
International Language resources and Evaluation , 2010.

31. Thank you!

32. Questions?