Sentiment analysis / classification with MaxEnt

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Transcript Sentiment analysis / classification with MaxEnt

Sentiment Analysis + MaxEnt*

MAS.S60

Rob Speer Catherine Havasi * Lots of slides borrowed for lots of sources! See end.

People on the Web have opinions

The world is full of text

• Customer verbatims • Blogs • Comments • Reviews • Forums

Measuring public opinion through social media?

People in U.S.

Query

I like Obama I do not

Write

Can we derive a similar measurement?

Query

Aggregate Text Sentiment Measure

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%

Application: Information Extraction “

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 Phillips AAAI05 Riloff, Wiebe, ICWSM 2008 6

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/operationcon dor/reviews/6na.html

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

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

Twitter Mood Swings

Alan Mislove, Northeastern

Daily Mood

Weekly Mood

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.

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 e.g., in news articles, etc, but they are usually known in the user generated content, i.e., authors of the posts.

: identify holders is also useful,

Bags of Words

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

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

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)

Corpus

• MPQA: www.cs.pitt.edu/mqpa/databaserelease (version 2) • 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.

ICWSM 2008 18

Gold Standards

• • Derived from manually annotated data Derived from “ found ” – Livejournal data Cambria, Havasi 2008 (examples) : – 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 ICWSM 2008 19

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

Patterns

• • • • 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 … ICWSM 2008 21

Conjunction

ICWSM 2008 22

*We cause great leaders

ICWSM 2008 23

Statistical association

• 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) ICWSM 2008 24

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)

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)

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)

Smoothed comparisons “jobs” sentiment

Beyond good and bad

• Can we identify excitement, embarrassment, fear, and all kinds of other emotions?

Sentiment as Topics

The Hourglass of Emotions

A quantified version of Robert Plutchik’s psychoevolutionary “wheel of emotions” (1980)

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: ...”

Learning valence

• Most classifiers are effectively learning a valence for every feature – funny = +1 – disappointed = -2 – seagal = -3

Naïve Bayes again?

• • Sure, okay But interesting n-grams clearly aren’t independent – “Gwyneth Paltrow” will be double-counted every time

Maximum Entropy (MaxEnt)

• • MaxEnt finds a probability distribution that follows a logistic curve Doesn’t require independence

The logistic (logit) distribution

• • • • • c = class d = data f = features in the data

= a value for every feature Z = whatever you need to divide by to make it add up to 1

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?

Maximum Likelihood

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

Slide Credits

• • • • Brendan O’Connor, CMU – OpinionFinder Carmen Banea /Jan Wiebe Luminoso Smilies: Aditya Joshi