sa-mincut-aditya.ppt
- 1. Sentiment Analysis Presented by Aditya Joshi 08305908 Guided by Prof. Pushpak Bhattacharyya IIT Bombay
- 2. What is SA & OM? • Identify the orientation of opinion in a piece of text • Can be generalized to a wider set of emotions The movie was fabulous! The movie stars Mr. X The movie was horrible!
- 3. Motivation • Knowing sentiment is a very natural ability of a human being. Can a machine be trained to do it? • SA aims at getting sentiment-related knowledge especially from the huge amount of information on the internet • Can be generally used to understand opinion in a set of documents
- 4. Tripod of Sentiment Analysis Cognitive Science Natural Language Processing Machine Learning Sentiment Analysis Natural Language Processing Machine Learning
- 6. Challenges • Contrasts with standard text-based categorization • Domain dependent • Sarcasm • Thwarted expressions Mere presence of words is Indicative of the category in case of text categorization. Not the case with sentiment analysis • Contrasts with standard text-based categorization • Domain dependent • Sarcasm • Thwarted expressions Sentiment of a word is w.r.t. the domain. Example: ‘unpredictable’ For steering of a car, For movie review, • Contrasts with standard text-based categorization • Domain dependent • Sarcasm • Thwarted expressions Sarcasm uses words of a polarity to represent another polarity. Example: The perfume is so amazing that I suggest you wear it with your windows shut • Contrasts with standard text-based categorization • Domain dependent • Sarcasm • Thwarted expressions the sentences/words that contradict the overall sentiment of the set are in majority Example: The actors are good, the music is brilliant and appealing. Yet, the movie fails to strike a chord.
- 7. SentiWordNet •Lexical resource for sentiment analysis •Built on the top of WordNet synsets •Attaches sentiment-related information with synsets
- 8. Quantifying sentiment Objective Polarity Subjective Polarity Positive Negative Term sense position Each term has a Positive, Negative and Objective score. The scores sum to one.
- 9. Building SentiWordNet • Ln, Lo, Lp are the three seed sets • Iteratively expand the seed sets through K steps • Train the classifier for the expanded sets
- 10. Lp Ln Expansion of seed sets The sets at the end of kth step are called Tr(k,p) and Tr(k,n) Tr(k,o) is the set that is not present in Tr(k,p) and Tr(k,n)
- 11. Committee of classifiers • Train a committee of classifiers of different types and different K-values for the given data • Observations: – Low values of K give high precision and low recall – Accuracy in determining positivity or negativity, however, remains almost constant
- 12. WordNet Affect • Similar to SentiWordNet (an earlier work) • WordNet-Affect: WordNet + annotated affective concepts in hierarchical order • Hierarchy called ‘affective domain labels’ – behaviour – personality – cognitive state
- 13. Subjectivity detection • Aim: To extract subjective portions of text • Algorithm used: Minimum cut algorithm
- 14. Constructing the graph • Why graphs? • Nodes and edges? • Individual Scores • Association scores To model item-specific and pairwise information independently. • Why graphs? • Nodes and edges? • Individual Scores • Association scores Nodes: Sentences of the document and source & sink Source & sink represent the two classes of sentences Edges: Weighted with either of the two scores • Why graphs? • Nodes and edges? • Individual Scores • Association scores Prediction whether the sentence is subjective or not Indsub(si)= • Why graphs? • Nodes and edges? • Individual Scores • Association scores Prediction whether two sentences should have the same subjectivity level T : Threshold – maximum distance upto which sentences may be considered proximal f: The decaying function i, j : Position numbers
- 15. Constructing the graph • Build an undirected graph G with vertices {v1, v2…,s, t} (sentences and s,t) • Add edges (s, vi) each with weight ind1(xi) • Add edges (t, vi) each with weight ind2(xi) • Add edges (vi, vk) with weight assoc (vi, vk) • Partition cost:
- 17. Document Subjective Results (1/2) • Naïve Bayes, no extraction : 82.8% • Naïve Bayes, subjective extraction : 86.4% • Naïve Bayes, ‘flipped experiment’ : 71 % Document Subjectivity detector Objective POLARITY CLASSIFIER
- 18. Results (2/2)
- 19. Approach 1: Using adjectives • Many adjectives have high sentiment value – A ‘beautiful’ bag – A ‘wooden’ bench – An ‘embarrassing’ performance • An idea would be to augment this polarity information to adjectives in the WordNet
- 20. Setup • Two anchor words (extremes of the polarity spectrum) were chosen • PMI of adjectives with respect to these adjectives is calculated Polarity Score (W)= PMI(W,excellent) – PMI (W, poor) excellent poor word PMI PMI
- 21. Experimentation • K-means clustering algorithm used on the basis of polarity scores • The clusters contain words with similar polarities • These words can be linked using an ‘isopolarity link’ in WordNet
- 22. Results • Three clusters seen • Major words were with negative polarity scores • The obscure words were removed by selecting adjectives with familiarity count of 3 – the ones that are not very common
- 23. Approach 2: Using Adverb- Adjective Combinations (AACs) • Calculate sentiment value based on the effect of adverbs on adjectives • Linguistic ideas: • Adverbs of affirmation: certainly • Adverbs of doubt: possibly • Strong intensifying adverbs: extremely • Weak intensifying adverbs: scarcely • Negation and Minimizers: never
- 24. Moving towards computation… • Based on type of adverb, the score of the resultant AAC will be affected • Example of an axiom: • Example : ‘extremely good’ is more positive than ‘good’
- 25. AAC Scoring Algorithms 1. Variable Scoring Algorithm 2. Adjective Priority Scoring Algorithm 3. Adverb first scoring algorithm
- 26. Scoring the sentiment on a topic • Rel (t) : Sentences in d that reference to topic t • s : Sentence is Rel (t) • Appl+(s) : AACs with positive score in s • Appl-(s) : AACs with negative score in s • Return strength =
- 27. Findings • APSr with r=0.35 worked the best (Better correlation with human subject) – Adjectives are more important than adverbs in terms of sentiment • AACs give better precision and recall as compared to only adjectives
- 28. Approach 3: Subject-based SA • Examples: The horse bolted. The movie lacks a good story.
- 29. Lexicon subj. bolt b VB bolt subj subj. lack obj. b VB lack obj ~subj Argument that sends the sentiment (subj./obj.) Argument that receives the sentiment (subj./obj.) Argument that receives the sentiment (subj./obj.)
- 30. Lexicon • Also allows ‘S+’ characters • Similar to regular expressions • E.g. to put S+ to risk – The favorability of the subject depends on the favorability of ‘S+’.
- 31. Example The movie lacks a good story. G JJ good obj. The movie lacks S+. B VB lack obj ~subj. Lexicon : Steps : 1) Consider a context window of upto five words 2) Shallow parse the sentence 3) Step-by-step calculate the sentiment value based on lexicon and by adding ‘S+’ characters at each step
- 32. Results Description Precision Recall Benchmark corpus Mixed statements 94.3% 28% Open Test corpus Reviews of a camera 94% 24%
- 33. Applications • Review-related analysis • Developing ‘hate mail filters’ analogous to ‘spam mail filters’ • Question-answering (Opinion-oriented questions may involve different treatment)
- 34. Conclusion & Future Work • Lexical Resources have been developed to capture sentiment-related nature • Subjective extracts provide a better accuracy of sentiment prediction • Several approaches use algorithms like Naïve Bayes, clustering, etc. to perform sentiment analysis • The cognitive angle to Sentiment Analysis can be explored in the future
- 35. References (1/2) • Tetsuya Nasukawa, Jeonghee Yi. ‘Sentiment Analysis: Capturing Favorability Using Natural Language Processing’. In K-CAP ’03, Florida, pages 1-8. 2003. • Alekh Agarwal, Pushpak Bhattacharyya. ‘Augmenting WordNet with polarity information on adjectives’. In K-CAP ’03, Florida, pages 1-8. 2003. • SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining Andrea Esuli, Fabrizio Sebastiani • ‘Machine Learning’, Han and Kamber, 2nd edition, 310-330. • http://wordnet.princeton.edu • Farah Benamara, Carmine Cesarano, Antonio Picariello, VS Subrahmanian et al; ‘Sentiment Analysis: Adjectives and Adverbs are better than Adjectives Alone’; In ICWSM ’2007 Boulder, CO USA, 2007.
- 36. References (2/2) • Jon M. Kleinberg; ‘Authoritative Sources in a Hyperlinked Environment’ as IBM Research Report RJ 10076, May 1997, Pgs. 1 – 34. • www.cs.uah.edu/~jrushing/cs696-summer2004/notes/Ch8Supp.ppt • Opinion Mining and Sentiment Analysis, Foundations and Trends in Information Retrieval, B. Pang and L. Lee, Vol. 2, Nos. 1–2 (2008) 1–135, 2008. • Bo Pang, Lillian Lee; ‘A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts’; Proceedings of the 42nd ACL; pp. 271–278; 2004. • http://www.cse.iitb.ac.in/~veeranna/ppt/Wordnet-Affect.ppt