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Sentiwordnet [IIT-Bombay]

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A presentation describing Sentiwordnet - a dictionary of synsets annotated with their sentiment.

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Sentiwordnet [IIT-Bombay]

  1. 1. Paper Presentation SentiWordNet by Andrea Esuli and Fabrizio Sebastiani Sagar Ahire [133050073]
  2. 2. Roadmap ● ● ● ● Introduction to Sentiment Analysis Introduction to Sentiwordnet Building of Sentiwordnet Enhancements in 3.0
  3. 3. Roadmap: We Are Here ● ● ● ● Introduction to Sentiment Analysis Introduction to Sentiwordnet Building of Sentiwordnet Enhancements in 3.0
  4. 4. Introduction to Sentiment Analysis ● The task of identifying the opinion expressed by a document. ● Can be carried out at various levels: ○ ○ ○ ○ Word level Sentence level Document level Aspect level, etc.
  5. 5. Tasks in Sentiment Analysis ● Determining Text SO-Polarity ○ Subjective vs. Objective ● Determining Text PN-Polarity ○ Positive vs. Negative ● Determining Strength of Text PN-Polarity ○ Weakly Positive vs. Strongly Positive ○ Weakly Negative vs. Strongly Negative ○ Star Rating
  6. 6. Tasks in Sentiment Analysis ● Determining Text SO-Polarity ○ Subjective vs. Objective ● Determining Text PN-Polarity ○ Positive vs. Negative ● Determining Strength of Text PN-Polarity ○ Weakly Positive vs. Strongly Positive ○ Weakly Negative vs. Strongly Negative ○ Star Rating
  7. 7. Tasks in Sentiment Analysis ● Determining Text SO-Polarity ○ Subjective vs. Objective ● Determining Text PN-Polarity ○ Positive vs. Negative ● Determining Strength of Text PN-Polarity ○ Weakly Positive vs. Strongly Positive ○ Weakly Negative vs. Strongly Negative ○ Star Rating
  8. 8. Roadmap: We Are Here ● ● ● ● Introduction to Sentiment Analysis Introduction to Sentiwordnet Building of Sentiwordnet Enhancements in 3.0
  9. 9. Introduction to Sentiwordnet ● Sentiwordnet is a sentiment lexicon associating sentiment information to each wordnet synset. ● Sentiwordnet = Wordnet + Sentiment Information
  10. 10. Sentiment Information For each wordnet synset s, the following information is available in Sentiwordnet: ● Positive Score Pos(s) ● Negative Score Neg(s) ● Objective Score Obj(s) Pos(s) + Neg(s) + Obj(s) = 1
  11. 11. Roadmap: We Are Here ● ● ● ● Introduction to Sentiment Analysis Introduction to Sentiwordnet Building of Sentiwordnet Enhancements in 3.0
  12. 12. Building Sentiwordnet ● Trained a set of 8 ternary (P vs. N vs. O) classifiers, differing in ○ Training Set ○ Learning Algorithm ● Scored each synset based on no of classifiers: ○ P score = No of classifiers stating Positive / 8 ○ N score = No of classifiers stating Negative / 8 ○ O score = No of classifiers stating Objective / 8
  13. 13. Classifiers: Training Sets ● Used semi-supervised approach starting with a seed set of paradigmatic synsets (such as nice, nasty, etc.) ● Performed ‘k’ iterations of expansion using Wordnet lexical relations ○ ○ ○ ○ ○ ○ Direct antonymy Similarity Derived from Pertains to Attribute Also see
  14. 14. Classifiers: Training Sets ● Obtained 4 training sets for the following ‘k’: ○ ○ ○ ○ 0 2 4 6
  15. 15. Classifiers: Learning Algorithms ● The learning algorithms used were: ○ SVM ○ Rocchio ● Thus all combinations of 4 training sets and 2 learners yield 8 classifiers
  16. 16. Classifiers: Assigning Categories ● Each ternary classifier is a sum of 2 binary classifiers: ○ Positive vs. Not Positive ○ Negative vs. Not Negative ● Categories are assigned as: P NP N Objective Negative NN Positive Objective
  17. 17. Classifiers: Observations ● Effect of ‘k’: ○ Low ‘k’ -> Low Recall, High Precision ○ High ‘k’ -> High Recall, Low Precision ● Effect of learning algorithm: ○ SVM -> Favours set with higher cardinality ○ Rocchio -> Equal prior probabilities
  18. 18. Statistical Results: Average Scores Part of Speech Positive Negative Objective Adjectives 0.106 0.151 0.743 Names 0.022 0.034 0.944 Verbs 0.026 0.034 0.940 Adverbs 0.235 0.067 0.698 All 0.043 0.054 0.903
  19. 19. Roadmap: We Are Here ● ● ● ● Introduction to Sentiment Analysis Introduction to Sentiwordnet Building of Sentiwordnet Enhancements in 3.0
  20. 20. Random Walk ● Views Wordnet as a graph and performs random walk on it ● Updates P, N and O values till process converges ● Edge from s1 to s2 if s1 occurs in gloss of s2
  21. 21. Random Walk ● Two random walks are performed: ○ P Score ○ N Score ● O Score is assigned so that P + N + O = 1
  22. 22. Website Sentiwordnet is available at: http://sentiwordnet.isti.cnr.it
  23. 23. Major References ● SentiWordNet: A Publicly Available Lexical Resource for Opinion Mining by Andrea Esuli, Fabrizio Sebastiani, 2006 ● SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining by Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani, 2010
  24. 24. Other References ● Sentiment Analysis and Opinion Mining by Bing Liu, 2012
  25. 25. Further Plan ● Wordnet-Affect (2004) by Carlo Strapparava, Alessandro Valitutti in proceedings of the 4th International Conference of Language Resources and Evaluation (LREC), Lisbon - IN PROGRESS ● Lexicon-based Methods in Sentiment Analysis (2011) by Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, Manfred Stede in the Journal of Computational Linguistics

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