Unsupervised Sentiment Analysis Using Item Response Theory Models
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1 Unsupervised Sentiment Analysis Using Item Response Theory Models Nathan Danneman NLP DC March 12, 2014 Nathan Danneman IRT Models NLP DC Mar 12, / 24
2 Table of Contents 1 Introductions 2 IRT History Nathan Danneman IRT Models NLP DC Mar 12, / 24
3 Introductions Introductions About me. Nathan Danneman IRT Models NLP DC Mar 12, / 24
4 Introductions Introductions About me. Nathan Danneman IRT Models NLP DC Mar 12, / 24
5 Introductions Introductions About me. About Data Tactics. Nathan Danneman IRT Models NLP DC Mar 12, / 24
6 Introductions Introductions About me. About Data Tactics. About you. Nathan Danneman IRT Models NLP DC Mar 12, / 24
7 Introductions What is Sentiment? Nathan Danneman IRT Models NLP DC Mar 12, / 24
8 Introductions Why Do I Care? Availability of sentiment-laden text Sentiments are outcomes of interest Sentiments are strong predictors Nathan Danneman IRT Models NLP DC Mar 12, / 24
9 Introductions Why Do I Care? Availability of sentiment-laden text Sentiments are outcomes of interest Sentiments are strong predictors Nathan Danneman IRT Models NLP DC Mar 12, / 24
10 Introductions Why Do I Care? Availability of sentiment-laden text Sentiments are outcomes of interest Sentiments are strong predictors Nathan Danneman IRT Models NLP DC Mar 12, / 24
11 Introductions Current Approaches I: Lexicon-Based How to: 1 Make or obtain a dictionary of sentiment-laden terms 2 Count number of positive and negative terms that occur in each document 3 Aggregate those counts Problems: Stock dictionary: (too) general; single-language Custom dictionary: difficult, biased Aggregation:? Nathan Danneman IRT Models NLP DC Mar 12, / 24
12 Introductions Current Approaches I: Lexicon-Based How to: 1 Make or obtain a dictionary of sentiment-laden terms 2 Count number of positive and negative terms that occur in each document 3 Aggregate those counts Problems: Stock dictionary: (too) general; single-language Custom dictionary: difficult, biased Aggregation:? Nathan Danneman IRT Models NLP DC Mar 12, / 24
13 Introductions Current Approaches 2: Model-Based How to: 1 Tag (i.e. hand-code) some documents 2 Train a model of pr(positive) 3 Assignment: hard or probabilistic Problems: Tagging is slow, biased Model fitting, can be tough (large p) Naive Bayes handles large p but estimates pr(positive) poorly Nathan Danneman IRT Models NLP DC Mar 12, / 24
14 Introductions Current Approaches 2: Model-Based How to: 1 Tag (i.e. hand-code) some documents 2 Train a model of pr(positive) 3 Assignment: hard or probabilistic Problems: Tagging is slow, biased Model fitting, can be tough (large p) Naive Bayes handles large p but estimates pr(positive) poorly Nathan Danneman IRT Models NLP DC Mar 12, / 24
15 Introductions Barriers to an Unsupervised Approach Large p Sparse variables Single underlying dimension Nathan Danneman IRT Models NLP DC Mar 12, / 24
16 IRT: Context Item Response Theory (IRT) is both a theory, and a class of statistical models. Developed in psychometrics to evaluate test takers. Now the dominant paradigm for: Scoring tests (knowledge, aptitude, psychosis...any latent trait) Scaling the votes of voters (e.g. Senators, UN General Assembly, etc) Nathan Danneman IRT Models NLP DC Mar 12, / 24
17 IRT: Context Problem: assign people a math aptitude on the basis of a test. Nathan Danneman IRT Models NLP DC Mar 12, / 24
18 IRT: Context Problem: assign people a math aptitude on the basis of a test. Classical Test Theory: aptitude = proportion correct. A poor measure: doesn t account for the difficulty of each item. Nathan Danneman IRT Models NLP DC Mar 12, / 24
19 IRT: Context New (2-part) Problem: 1 Can t correctly estimate the aptitude of each student without knowing how difficult each question is. 2 Can t correctly estimate the difficulty of each question without knowing the aptitude of each student. Nathan Danneman IRT Models NLP DC Mar 12, / 24
20 IRT: Definition IRT allows us to estimate these things simultaneously. Let s denote students, q denote questions, and y be a student-by-question matrix populated by 1 s if student s got question q right, and 0 otherwise. Then estimate: Student q1 q2 q3... John Mary Katy pr(y s,q = 1) = exb 1+e xb xb = b 0,q + b 1,q x s b 0,q : difficulty (note the negative) b 1,q : discrimination x s : math ability Nathan Danneman IRT Models NLP DC Mar 12, / 24
21 IRT: Outcome on ONE Example Question pr(y s = 1) = logit( difficulty q + discrimination q ability s ) pr(correct) scaled ability Difficulty = 1 Discrimination = 2.5 Nathan Danneman IRT Models NLP DC Mar 12, / 24
22 IRT: Effect of Discrimination Parameter pr(y s = 1) = logit( difficulty q + discrimination q ability s ) pr(correct) scaled ability Difficulty = 1 Discrimination = 0.75 Difficulty = 1 Discrimination = 2.5 Nathan Danneman IRT Models NLP DC Mar 12, / 24
23 IRT: Effect of Difficulty Parameter pr(y s = 1) = logit( difficulty q + discrimination q ability s ) pr(correct) scaled ability Difficulty = 3 Discrimination = 2.5 Difficulty = 1 Discrimination = 2.5 Nathan Danneman IRT Models NLP DC Mar 12, / 24
24 An Aside: IRT in Political Science Political scientists wanted to scale voters; IRT is a natural fit. Now, let senators, s, vote on a set of bills, b. Additionally, allow b 1,bill (the discrimination parameter) to be positive or negative. Nathan Danneman IRT Models NLP DC Mar 12, / 24
25 IRT for Sentiment Analysis Input: a document-term (or document-bigram) matrix, where all counts are thresholded at 1. Outputs: a scaled value for each document; discrimination and difficulty parameters for each term (or bigram) Note 1: You simultaneously scale documents and induce a dictionary Note 2: You get confidence intervals on all of the above quantities Nathan Danneman IRT Models NLP DC Mar 12, / 24
26 Warning: Strong Assumptions Necessary To use IRT for sentiment analysis, the following must be true: Assumption 1: You have a collection of documents about the same thing. Assumption 2: Authors/texts lie along a single underlying continuum. Assumption 3: The continuum in Assumption 2 is sentiment. Assumption 4: The continuum in Assumptions 2 and 3 affects word usage monotonically. Nathan Danneman IRT Models NLP DC Mar 12, / 24
27 Warning: Strong Assumptions Necessary To use IRT for sentiment analysis, the following must be true: Assumption 1: You have a collection of documents about the same thing. Assumption 2: Authors/texts lie along a single underlying continuum. Assumption 3: The continuum in Assumption 2 is sentiment. Assumption 4: The continuum in Assumptions 2 and 3 affects word usage monotonically. Nathan Danneman IRT Models NLP DC Mar 12, / 24
28 Warning: Strong Assumptions Necessary To use IRT for sentiment analysis, the following must be true: Assumption 1: You have a collection of documents about the same thing. Assumption 2: Authors/texts lie along a single underlying continuum. Assumption 3: The continuum in Assumption 2 is sentiment. Assumption 4: The continuum in Assumptions 2 and 3 affects word usage monotonically. Nathan Danneman IRT Models NLP DC Mar 12, / 24
29 Warning: Strong Assumptions Necessary To use IRT for sentiment analysis, the following must be true: Assumption 1: You have a collection of documents about the same thing. Assumption 2: Authors/texts lie along a single underlying continuum. Assumption 3: The continuum in Assumption 2 is sentiment. Assumption 4: The continuum in Assumptions 2 and 3 affects word usage monotonically. Nathan Danneman IRT Models NLP DC Mar 12, / 24
30 IRT by Example I scraped about 4000 tweets containing uncbball or dukebball Note: at first I violated several assumptions. Dropped punctuation; changed to lower case; stemmed; created bigram doc-term matrix; aggregated up to level of author; removed bigrams used by only one author, and authors with 1 or less bigram. Estimated the model with a call to [ideal] in the [pscl] package in R. Took about 1 minute on my laptop. Nathan Danneman IRT Models NLP DC Mar 12, / 24
31 IRT by Example I scraped about 4000 tweets containing uncbball or dukebball Note: at first I violated several assumptions. Dropped punctuation; changed to lower case; stemmed; created bigram doc-term matrix; aggregated up to level of author; removed bigrams used by only one author, and authors with 1 or less bigram. Estimated the model with a call to [ideal] in the [pscl] package in R. Took about 1 minute on my laptop. Nathan Danneman IRT Models NLP DC Mar 12, / 24
32 Scaled Positions of Authors (Not Uniquely Identified!) Frequency Scaled Position Nathan Danneman IRT Models NLP DC Mar 12, / 24
33 Examples from Endpoints It s important to verify any latent variable model! On examination, negative numbers were UNC fans, and positive numbers were Duke fans. Ex: F@ck duke, go heels! #tarheels -1.8 Ex. Go devils, rematch at Cameron, #goblue #dukebball 0.85 Nathan Danneman IRT Models NLP DC Mar 12, / 24
34 Examining the Bigrams IRT History discrimination difficulty Nathan Danneman IRT Models NLP DC Mar 12, / 24
35 Examples of Discriminating Bigrams Examine the dictionary you ve created to make sure it makes sense. at cameron 12.2 go devils 11.6 tar heels -7.3 duck fook -4.9 Nathan Danneman IRT Models NLP DC Mar 12, / 24
36 Overview and Next Steps What have we learned? In certain cases, unsupervised sentiment analysis is possible You can simultaneously estimate word weights and author positions What s next? Move to a graded response model A richer model of zeroes Nathan Danneman IRT Models NLP DC Mar 12, / 24
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