Sentiment Analysis + MaxEnt *

1. Sentiment Analysis + MaxEnt* MAS.S60 Rob Speer Catherine Havasi * Lots of slides borrowed for lots of sources! See end.

2. People on the Web have opinions

3. The world is full of text • Customer verbatims • Blogs • Comments • Reviews • Forums

4. Measuring public opinionthrough social media? People in U.S. Query I like Obama Can we derive a similar measurement? I do not Aggregate Text Sentiment Measure Query Write

5. Anne Hathaway • Oct. 3, 2008 - Rachel Getting Married opens: BRK.A up .44% • Jan. 5, 2009 - Bride Wars opens: BRK.A up 2.61% • Feb. 8, 2010 - Valentine's Day opens: BRK.A up 1.01% • March 5, 2010 - Alice in Wonderland opens: BRK.A up .74% • Nov. 24, 2010 - Love and Other Drugs opens: BRK.A up 1.62% • Nov. 29, 2010 - Anne announced as co-host of the Oscars: BRK.A up .25%

6. Application: Information Extraction ICWSM 2008 “The Parliamentexplodedinto fury against the government when word leaked out…” Observation: subjectivity often causes false hits for IE Goal: augment the results of IE Subjectivity filtering strategies to improve IERiloff, Wiebe, Phillips AAAI05

7. Sentiment can be hard to analyze “This is where the money was spent, on well-choreographed kung-fu sequences, on giant Kevlar hamster balls, on smashed-up crates of bananas, and on scorpions. Ignore the gaping holes in the plot (how, exactly, if the villain's legs were broken, did he escape from the secret Nazi base, and why didn't he take the key with him?). Don't worry about the production values, or what, exactly, the Japanese girl was doing hitchhiking across the Sahara. Just go see the movie.” • http://www.killermovies.com/o/operationcondor/reviews/6na.html

8. Thwarted Expectations Narrative • “I thought it was going to be amazing… but it’s not unless you’re a hungover college student.” – Tripadvisor, Amy’s Café

9. This is a messy task • Inter-annotator agreement on sentiment analysis tasks can be as low as 70% • Pang et al., 2002: adding n-grams doesn’t seem to help

10. Twitter Mood Swings Alan Mislove, Northeastern

11. Daily Mood

12. Weekly Mood

13. Opinion mining tasks • At the document (or review) level: • Task: sentiment classification of reviews • Classes: positive, negative, and neutral • Assumption: each document (or review) focuses on a single object (not true in many discussion posts) and contains opinion from a single opinion holder. • At the sentence level: • Task 1: identifying subjective/opinionated sentences • Classes: objective and subjective (opinionated) • Task 2: sentiment classification of sentences • Classes: positive, negative and neutral. • Assumption: a sentence contains only one opinion; not true in many cases. • Then we can also consider clauses or phrases.

14. Opinion Mining Tasks (cont.) • At the feature level: • Task 1: Identify and extract object features that have been commented on by an opinion holder (e.g., a reviewer). • Task 2: Determine whether the opinions on the features are positive, negative or neutral. • Task 3: Group feature synonyms. • Produce a feature-based opinion summary of multiple reviews. • Opinion holders: identify holders is also useful, e.g., in news articles, etc, but they are usually known in the user generated content, i.e., authors of the posts.

15. Bags of Words • Look for certain keywords • “Valence” of a word • Advantage: Works quickly • Disadvantage: Lexical Creativity

16. Sentiment analysis: word counting • Subjectivity Clues lexicon from OpinionFinder / U Pitt • Wilson et al 2005 • 2000 positive, 3600 negative words • Procedure • Within topical messages, • Count messages containing these positive and negative words

17. Main resources • Annotated corpora • Used in statistical approaches (Hu & Liu 2004, Pang & Lee 2004) • MPQA corpus (Wiebe et. al, 2005) • Tools • Algorithm based on minimum cuts (Pang & Lee, 2004) • OpinionFinder (Wiebe et. al, 2005) • Lexicons • General Inquirer (Stone et al., 1966) • OpinionFinder lexicon (Wiebe & Riloff, 2005) • SentiWordNet (Esuli & Sebastiani, 2006)

18. Corpus ICWSM 2008 • MPQA: www.cs.pitt.edu/mqpa/databaserelease (version 2) • English language versions of articles from the world press (187 news sources) • Also includes contextual polarityannotations • Themes of the instructions: • No rules about how particular words should be annotated. • Don’t take expressions out of contextand think about what they could mean,but judge them as they are used in that sentence.

19. Gold Standards ICWSM 2008 • Derived from manually annotated data • Derived from “found” data (examples): • LivejournalCambria, Havasi 2008 • Blog tags Balog, Mishne, de Rijke EACL 2006 • Websites for reviews, complaints, political arguments • amazon.comPang and Lee ACL 2004 • complaints.comKim and Hovy ACL 2006 • bitterlemons.comLin and Hauptmann ACL 2006 • Word lists (example): • General Inquirer Stone et al. 1996

20. A note on the sentiment list • This list is not well suited for social media English. • “sucks”, “ :) ”, “ :( ” (Top examples) word valence count will positive 3934 bad negative 3402 good positive 2655 help positive 1971 (Random examples) word valence count funny positive 114 fantastic positive 37 cornerstone positive 2 slump negative 85 bearish negative 17 crackdown negative 5

21. Patterns ICWSM 2008 Lexico-syntactic patternsRiloff & Wiebe 2003 way with <np>:… to ever let China use force to have its way with … expense of <np>: at the expense of the world’s security and stability underlined <dobj>: Jiang’s subdued tone … underlined his desire to avoid disputes …

22. Conjunction ICWSM 2008

23. *We cause great leaders ICWSM 2008

24. Statistical association ICWSM 2008 • If words of the same orientation likely to co-occur together, then the presence of one makes the other more probable (co-occur within a window, in a particular context, etc.) • Use statistical measures of association to capture this interdependence • E.g., Mutual Information (Church & Hanks 1989)

25. Sentiment Ratio Moving Average • High day-to-day volatility. • Average last k days. • Keyword “jobs”, k = 1, 7, 30 • (Gallup tracking polls: 3 or 7-day smoothing)

26. Sentiment Ratio Moving Average • High day-to-day volatility. • Average last k days. • Keyword “jobs”, k = 1, 7, 30 • (Gallup tracking polls: 3 or 7-day smoothing)

27. Sentiment Ratio Moving Average • High day-to-day volatility. • Average last k days. • Keyword “jobs”, k = 1, 7, 30 • (Gallup tracking polls: 3 or 7-day smoothing)

28. Smoothed comparisons“jobs” sentiment

29. Smoothed comparisons“jobs” sentiment

30. Smoothed comparisons“jobs” sentiment

31. Smoothed comparisons“jobs” sentiment

32. Smoothed comparisons“jobs” sentiment

33. Smoothed comparisons“jobs” sentiment

34. Smoothed comparisons“jobs” sentiment

35. Smoothed comparisons“jobs” sentiment

36. Smoothed comparisons“jobs” sentiment

37. Smoothed comparisons“jobs” sentiment

38. Smoothed comparisons“jobs” sentiment

39. Smoothed comparisons“jobs” sentiment

40. Smoothed comparisons“jobs” sentiment

41. Smoothed comparisons“jobs” sentiment

42. Smoothed comparisons“jobs” sentiment

43. Smoothed comparisons“jobs” sentiment

44. Beyond good and bad • Can we identify excitement, embarrassment, fear, and all kinds of other emotions?

45. Sentiment as Topics

46. The Hourglass of Emotions A quantified version of Robert Plutchik’spsychoevolutionary “wheel of emotions” (1980)

47. SenticNet • Augments ConceptNet with emotion-tagged data • Learns a function from semantic vectors to the emotion space • Evaluation: classify LJ posts that are tagged with “Current mood: ...”

48. Learning valence • Most classifiers are effectively learning a valencefor every feature • funny = +1 • disappointed = -2 • seagal = -3

49. Naïve Bayes again? • Sure, okay • But interesting n-grams clearly aren’t independent • “Gwyneth Paltrow” will be double-counted every time

50. Maximum Entropy (MaxEnt) • MaxEnt finds a probability distribution that follows a logistic curve • Doesn’t require independence