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

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 20086 “The Parliament exploded into 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 IE Riloff, Wiebe, Phillips AAAI05

7. Sentiment can be hard to analyze http://www.killermovies.com/o/operationcon dor/reviews/6na.html “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.”

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 1.Within topical messages, 2.Count messages containing these positive and negative words

17. Main resources Lexicons General Inquirer (Stone et al., 1966) OpinionFinder lexicon (Wiebe & Riloff, 2005) SentiWordNet (Esuli & Sebastiani, 2006) 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)

18. Corpus ICWSM 200818 MPQA: www.cs.pitt.edu/mqpa/databaserelease (version 2) MPQA: www.cs.pitt.edu/mqpa/databaserelease English language versions of articles from the world press (187 news sources) Also includes contextual polarity annotations Themes of the instructions: – No rules about how particular words should be annotated. – Don’t take expressions out of context and think about what they could mean, but judge them as they are used in that sentence.

19. Gold Standards ICWSM 200819 Derived from manually annotated data Derived from “found” data (examples): – Livejournal Cambria, Havasi 2008 – Blog tags Balog, Mishne, de Rijke EACL 2006 – Websites for reviews, complaints, political arguments amazon.com Pang and Lee ACL 2004 complaints.com Kim and Hovy ACL 2006 bitterlemons.com Lin 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 200821 Lexico-syntactic patterns Riloff & Wiebe 2003 way with : … to ever let China use force to have its way with … expense of : at the expense of the world’s security and stability underlined : Jiang’s subdued tone … underlined his desire to avoid disputes …

22. Conjunction ICWSM 200822

23. *We cause great leaders ICWSM 200823

24. Statistical association ICWSM 200824 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

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’s psychoevolutionary “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 valence for 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

51. The logistic (logit) distribution c = class d = data f = features in the data λ = a value for every feature Z = whatever you need to divide by to make it add up to 1

52. MaxEnt learns a probability distribution Optimizing for two things: – Maximize the probability of your data –...but be as uninformative as possible about things missing from your data An unfair coin comes up heads four times. What’s the probability that it comes up heads the next time?

53. Maximum Likelihood

54. Maximum A Posteriori p(class | data) ∝ p(data | class) * p(class) posterior = likelihood * prior Our prior on p(class) can apply a penalty to large weights

55. Slide Credits Brendan O’Connor, CMU – OpinionFinder Carmen Banea /Jan Wiebe Luminoso Smilies: Aditya Joshi