1. Prof. Pushpak Bhattacharyya
Sentiment Analysis
PhD Seminar
Balamurali A R( )
Under guidance of 
Prof. Pushpak Bhattacharyya
Dept of CSE-IIT Bombay
Mumbai

2. Outline Introduction Motivation Challenges General Model
Word level sentiment analysis
Sentence level sentiment analysis
Comparative sentence analysis
Document level sentiment analysis
Conclusion & Future works
References

3. Sentiment Analysis(SA) - Introduction
Advent of UGC – A two way communication.
Vast of information – Most of them direct feed backs
Objective: To fine Sentiment or opinion of a user with regard to an entity/object
Fine grain version of Subjectivity Analysis
Subjectivity Analysis - finding whether phrase, sentence, document is subjective or objective.
Web 2.0

4. Motivation Businesses and organizations:
Product and service benchmarking. Market intelligence.   
People:
Finding opinions while purchasing a new product    Finding opinions on political topics
Advertisement:
Placing ads in the user-generated content    Place an ad when one praises a product.    Place an ad from a competitor if one criticizes a product. 
Information search & Retrieval:
Providing general search for "opinions".
Include the motivation the report .
jancy weibe slides - read the motivation from wikipedia
Information search & Retrieval: say about the "Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews" " - the application part can be included in the report.

5. General Model Opinion holder (source) :person who holds the sentiment.
E.g. I love playing hockey.
E.g I agree to what pope said “hate the sin not the sinners” -<I,Pope>
Object (Target) :product, person, organization or a topic on which sentiment is expressed.
E.g. I like nano. But I don’t like the steering of nano.
Opinion/sentiment a view or appraisal on an object
E.g. It’s a pity(negative) that she didn’t marry.

6. Challenges some of the parts is in not equal to the whole- [Turney’02]
Identifying source and target:
some of the parts is in not equal to the whole- [Turney’02]
Movies and the themes included – how to separate the sentiment
“Movie was classic in fact Gabbar Singh was epitome of villainy!"
Differentiating feature and attributes
“I hate iPod, but I like the scroll technology”
Role of semantics
“How could anyone sit through this movie?”
Issue of Ideology- [Sack’ 92]
“Saddam Hussein” - Mixed opinion?????

7. Sentiment Analysis: How to do?
Sentiment Classification at Document level
Sentence level sentiment analysis.
Word level Sentiment Analysis
Comparative sentence analysis
OpinionFinder: A system for subjectivity analysis
resource for sentiment analysis

8. Word level Sentiment Analysis
Used for grammatically incoherent text – Short news paper headlines e.g. Almost Perfection [The Hindu’22/04/09]
Direct computation using lexical resource – SentiWordNet, WordNetAffect
SentiWordNet – wordnet graded with pos(c),neg(c)& obj(c) score. e.g. Love
Created using classifiers
Interesting Findings -Mostly opinionated content
carried by modifiers(adjective & adverbs)
e.g. smart IITian
source:
Lower most or naïve based analysis.
Words to sysnset shift for wordnet – different forms of same word got different polarity.
Say about “Mad”
Ternary classifier – positive, negative, objective – differ in training set, and learning algorithm
Features are glosses tf*idf

9. UPAR’07
Pre Process
POS tagging
Parsing
Dependency
Global Sentence Rating
Addition of Rules
Linear Combination of Words
Evaluation
E.g. “Manmohan insists troop stay in Guwhati, predicts midterm victory” System achieves valence accuracy of 55%
Manmohan/NNP
insists/VBZ
troop/NN
stay/NN
in/IN
Guwhati/NNP
,/,
predicts/VBZ
midterm/JJ
victory/NN
(ROOT (SINV (S (NP (NNP Manmohan)) (VP (VBZ insists) (NP (NP (NN troop) (NN stay)) (PP (IN in) (NP (NNP Guwhati)))))) (, ,) (VP (VBZ predicts)) (NP (JJ midterm) (NN victory))))
Sem eval 2007 paper – which implemented the hypothesis
Rule based system – which uses linguistic resources.
Emotions to boost – joy
Lessen – anger,disgust,sadness,fear,compassion

10. Sentence level Sentiment Analysis
Contextual information necessary for SA at sentence level
“Indian observers were not happy about things happening in its border country, even though west were enjoying the show.”
Cannot assign prior polarity to all words!
E.g long battery life & long time to recharge.
Issue of negation – not happy
Issue of syntactic role - Polluters are V/s they are polluters
Issue of neutral polarity – look forward to

11. How to include Contextual Polarity
Prior clues
Assumption-sentence polarity is product of clues
Different Clues Manually detected
Sub Detection
Classify sentence into polar/neutral
Create Feature Vector from Polar sentence
Polarity Classification
Disambiguate Contextual Polarity
Assign Polarity
Clues Manually detected
Positive,negative,both,neutral

12. Contd.
28 Features for Neutral-polar classifier with an accuracy of 75.9%
Polarity classifiers used 10 features for classification.
Polarity classifier achieved an accuracy of 65.7%

13. Comparative sentence analysis
A preferential emotion detection
“I like Prof. X class to Prof. Y class”
More Related to Opinion Mining
Common Feature – presence of comparison word e.g. IIT Bombay is better than IIT Y
Comparison word may/may not opinionated(emotional state) – e.g longer
Preference of sentence with opinionated sentence easy
Find context -> <features, opinion comparison word><battery Life ,longer>
Use context to determine opinion orientation- will be explained later.
10% of user
Say about product reviews
Forum discussions… x compared with Y
When saying about “longer” example say about the contextual depended opinions – implicit opinion.

14. Comparative sentence analysis
Different types of comparatives
Non equal Gradable(less than),Equative(same),Superlative(longest),
NonGradable(Nano and supera has got different features)
Comparative Relation(CR)<long,battery,S1,S2>
Objective – Given CR, to find S1 or S2 is preferred.
Some more Categories of Comparatives
Type 1 (er,est), Type 2( more, most) , Increasing comparatives (longer), Decreasing Comparatives(fewer)
Final analysis depend type of comparative word(C) and feature involved(F).
Opinionated comparative
Comparative with context dependent opinion (“higher milage”)
in different comparative types only 1 and 3 are focused because only these 2 contain
Use context to disambiguate theorientation of the comparitive words.
Categories are adjectives or adverbs –type 1 er or est to base adjectives
Type 2 these are types in which more ,most has to be added to show comparitive or superlative degree.
Comparative orginates from adj or adv – there will be sentiment associated with it.
Type 1 worse, better
Type 2 add – adding more , less, most or least-
State 4 rules -<increasing comparative,feature>
Comparative

15. Comparative sentence analysis
C opinionated
e.g. Better
Assign S1 preferred
Only F opinionated
e.g. X makes more noise than Y
Use Comparative rules
Get preferred entity
Both C & F not opinionated
e.g. Long (battery) life
Use external source
Find OSA(F,C)=log(pr(f,c)pr(c|f)/(pr(f)pr(c)))
Decision rule is applied for preference
C – feature indicator
e.g. Nano is smaller than Indica
Count number of times it appears in cons and pros
If #pros(C) > #cons(C),S1 is preferred
Different cases
OSA – one sided association

16. Comparative sentence analysis
Baseline – default preference S1 -84%
System accuracy – 94%
Inferences
People usually give S1 more preference in comparative sentence

17. Sentiment Analysis – Document Level
To classify documents as positive or negative
e.g.“Manali travel review” – Recommended/Not recommended
Extract phrases - 2 words long(with context) e.g. good place
Calculate the semantic orientation of extracted phrases using PMI with “excellent” & “poor”
Classification based on Average semantic orientation of the phrases
Classify based on average threshold – Here its Zero
POS tag it – get the rules. And extract.
Identify the adjective and adverbs – why 2 words – to get the context – eg upredictable steering , unpredictable plot
Mention rules – 2 words – how is it extracted.
So is pmi(phrase,excellent)- pmi(phrase,poor)
So is positive when it has a good association”romantic ambience” else negative ”paltry system”
Uses PMI and near operator of altavista
Review is positive it average so is positive

18. Sentiment Analysis – Document Level
SO(phrase)=
e.g. “unethical practices”
Different categories were tested – Automobiles, Banks, Movies,
Travel destinations.
Average Accuracy 74%, except for movies.
Movies contain theme within expressing a sentiment
e.g Raj’s arrogance and sadistic mentality towards society is mercilessly shown by director Mani Ratnam. The film can be regarded as one of all time best nonfiction movie.”

19. Sentiment Analysis – Document Level: A graph based method
Base lined Version
Graph Based Min Cut system
Remove obj sentences from text -> then use polarity classifier
Should improve the final polarity detection
Adjuscent sentence share same subjectivity status. – coherence-a pair wise interaction status given.
Source: Pang& lee,2004

20. Contd.
Objective: Minimize
Individual score Non negative estimates of each xi preference for being in Cj
Association score assoc (xi,xj) –:Non negative estimate of how important it is that xi and xj are in the same class.
Solution: Create (G,V) = {v1, v2...vn, s, t} and partition into cuts of minimum costs.
In our case S,T would be subjective and objective
indj (si) = pr(si|sub)
Assoc(si, sj)= function of distance between si and sj
Accuracy improved from 85.2% to 86.4%

21. Conclusion & Future work
Different level of text requires different treatment for assessing the sentiment.
Domain of Text also play an important role.
Future work:
finding the target of the sentiment
Dealing with sarcasm
Multilingual sentiment analysis
Ideology and its handling

22. Reference
[1]. Warren Sack 1994,On the computation of point of view, Proceedings of the twelfth national conference on Artificial intelligence, 1994 pp [2]. Pang & Lee 2002, Thumbs up? Sentiment Classification using Machine Learning Technique, Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Philadelphia,ACL July 2002, pp [3].Peter Turney 2002, Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, July 2002, pp [4]. C Strapparava, A Valitutti ,2004 ,WordNet-Affect: an affective extension of WordNet ,Proceedings of LREC, Vol. 4 , pp [5]. Bo Pang and Lillian Lee 2004, A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts, Proceedings of ACL [6]. Wiebe.et.al 2005, Annotating Expressions of Opinions and Emotions in Language, Computers and the Humanities, Vol. 39, No (May 2005), pp [7]. Wilson Theresa, Wiebe Janyce, Hoffmann Paul. 2005, Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis, Proceedings of Human Language Technologies Conference/Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP 2005)

23. [8]. Wiebe, J. and Mihalcea, R. 2006. Word sense and subjectivity
[8]. Wiebe, J. and Mihalcea, R Word sense and subjectivity. In Proceedings of the 21st international Conference on Computational Linguistics and the 44th Annual Meeting of the Association For Computational Linguistics (Sydney, Australia, July , 2006) PP
[9]. Andrea Esuli, Fabrizio Sebastiani 2006,SENTIWORDNET: A publicly available lexical resource for opinion mining, In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC’06),pp.417—422
[10]. François-Régis Chaumartin 2007, UPAR7: A knowledge-based system for headline sentiment tagging, , Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007)Prague, Association for Computational Linguistics pages 422–425
[11]. Liu, Bing 2007, Web Data Mining, Springer, chapter 11
[12].Ganapathibhotla & Liu 2008, Mining Opinions in Comparative Sentences, Proceedings of the 22nd International Conference on Computational Linguistics, Coling 2008, pages 241–248
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