1. Sentiment Detection Naveen Sharma(02005010) PrateekChoudhary(02005016) Yashpal Meena(02005030) Under guidance Of Prof. Pushpak Bhattacharya

2. Outline Problem Statement Challenges Earlier Work and Traditional Approaches Recent Advances Conclusion/Future Directions

3. Sentiment Analysis What is Sentiment Analysis? –Determining the overall polarity of a given document Polarity: - Positive - Negative - Mixed - Neutral

4. Motivation Individual –Movie Reviews on web (Thumbs up or Thumbs down) Commercial –Feedback/evaluation forms. –Opinions about a product. –Recognizing and discarding “flames” on newsgroups. Political –Opinions on government policies eg. Iraq War, Taxation

5. Sentiment Analysis A type of Text Classification Other types of Text Classifications –Author based Classification –Topic Categorization Sentiment Analysis and Topic categorization –Topics - subject matter –Sentiments - opinion towards subject matter

6. Challenges Reference to multiple objects in the same document - The NR70 is trendy. T-Series is fast becoming obsolete. Dependence on the context of the document - “Unpredictable” plot ; “Unpredictable” performance Negations have to be captured - Monochrome display is not what the user wants –It is not like the movie is a total waste of time

7. Challenges(contd.) Metaphors/Similes - The metallic body is solid as a rock Part-of and Attribute-of relationships - The small keypad is inconvenient Subtle Expression - How can someone sit through this movie?

8. Earlier Work (First approaches) Naive Bayes Maximum Entropy Support Vector Machines

9. Naïve Bayes What is Naïve Bayesian Classifier Difficulty -More than few variables -More than few variables How to over come this difficulty - Independence of variables - Independence of variables

10. Naïve Bayes(Contd.) --- set of predefined feature vectors --- set of predefined feature vectors –Features can be representative words/word patterns Each document d represented by document vector Where n i (d) = no. of times feature vector f i occurs in d Assign a document d to class Where P(d) plays no role in selecting c*.

11. Naïve Bayes(contd.) Assuming f i s are independent, Naïve Bayes can be decomposed as Advantages:Simple Performs Well

12. Recent Advances An unsupervised learning algorithm Extract phrases from the review based on pattern of parts of speech tags. JJ = adjective NN = Noun Eg. Extracting 2 word patterns First word Second Word Third Word (Not extracted) JJ NN or NNS Anything JJJJ Not NN nor NNS

13. Unsupervised Learning(contd.) Estimate Semantic Orientation of extracted phrases PMI (Pointwise Mutual Information) as strength of semantic association PMI(word 1, word 2 ) = log 2 [ p(word 1 & word 2 )/ p(word 1 ) p(word 2 )] log 2 [ p(word 1 & word 2 )/ p(word 1 ) p(word 2 )] SO(phrase) = SO(phrase) = PMI (phrase, ”excellent”) – PMI (phrase, “poor”)

14. Unsupervised Learning(contd.) Determine the Semantic Orientation (SO) of the phrases Search on AltaVista SO (phrase) =

15. Unsupervised Learning(contd.) Calculate the average semantic orientation of phrases in the given review and classify the review as recommended if the av- erage is positive and otherwise not recommended.

16. Recent Advances(contd.) Subjectivity and min-cuts Approach by Pang and Lee –Step1: labeling sentences as subjective and objective. –Step2: applying standard machine learning classifier to the subjective extract.

17. Min cut approach(contd.) Formalization : Suppose we have n items x 1 …..x n to divide into classes C 1 and C 2 We need two types of scores: –Individual scores ind j (x i ) estimate of each x i ’s preference –Associative scores assoc(x i, x k ) estimate of importance of both being in the same class estimate of importance of both being in the same class

18. Min cut approach(contd.) Maximize individual preference Penalize tightly associated items in different classes Optimization problem: The formula for cost: Build an undirected graph G with vertices {v 1 ….v n, s, t} edge (s, v i ) ---- weight ind 1 (x i )

19. Min cut approach(contd.) edge (v i, t) – weight ind 2 (x i ) edge (v i, t) – weight ind 2 (x i ) edge (v i, v k ) –weight assoc(x i, x k ) edge (v i, v k ) –weight assoc(x i, x k ) Classification problem now reduces to finding minimum cuts in the graph

20. Min cut approach(contd.)

21. Advantages/Analysis: –Different algorithms –Maximum flow algorithms –N most subjective sentences. –Last N sentences –Most Subjective N sentences

22. Recent Advances Using linguistic knowledge and wordnet synonymy graphs – Agarwal and Bhattacharya On Movie reviews Bag of words features Strength of adjective:

23. Wordnet Approach(contd.) about and of sentences –About the movie (review) –Whats in the movie Two kinds of weights: –Individual weights :: probability estimates by an SVM classifier –Mutual weights:: tendency to fall in same category Physical separation –Paragraph boundaries Contextual similarity –Total adjective strength –Scaling and distance measure

24. Wordnet Approach(cont.) Minimum cut algorithm similar to Pang and Lee Mutual Similarity Coefficient f k is the kth feature F i (f k ) = 1 if kth feature present in document = 0 otherwise = 0 otherwise

25. Wordnet Approach(contd.) SVM trained to give Pr good and Pr bad SVM probabilities and MSC values – Weights Matrix Min cut Approach

26. Wordnet Approach(contd.) Analysis –Mutual relationships between documents –Graph cut technique as simple and powerful –Decline in accuracy with subjectivity –Wordnet - a useful lexicon resource

27. Conclusion/Future Directions Practical Utility Harder than other text classifications Traditional machine learning techniques don’t perform that well. Linguistic knowledge needs to be used –Eg. Wordnet Subjectivity extracts and mutual dependencies

28. Conclusion/Future Directions Better measure to incorporate linguistic knowledge Better measures for degree of similarity Formulation as multiclass problem –Eg. Emotional icons in messengers –May be helpful in building psychological profiles through newsgroup mails

29. References Alekh Agarwal and Pushpak Bhattacharyya, Sentiment Analysis: A New Approach for Effective Use of Linguistic Knowledge and Exploiting Similarities in a Set of Documents to be Classified, International Conference on Natural Language Processing ( ICON 05), IIT Kanpur, India, December, 2005 Bo Pang and Lillian Lee, A Sentimental Education:Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts, Proceedings of ACL, 2004. Bo Pang, Lillian Lee and Shivakumar Vaithyanathan, Thumbs Up? Sentiment Classification Using Machine Learning Techniques, Proceedings of EMNLP 2002,pp 79-86. Peter Turney. 2002. Thumbs up or thumbs down? Se-mantic orientation applied to unsupervised classication of reviews. In Proc. of the ACL.

30. Thank You