Thumbs up? Sentiment Classification using Machine Learning Techniques Jason Lewris, Don Chesworth “Okay, I’m really ashamed of it, but I enjoyed it. I.

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Presentation transcript:

Thumbs up? Sentiment Classification using Machine Learning Techniques Jason Lewris, Don Chesworth “Okay, I’m really ashamed of it, but I enjoyed it. I mean, I admit it’s a really awful movie.”

Introduction Compared results to: Simplistic, human methods Topic generation Three machine learning classification models: Naïve Bayes (NB) Maximum Entropy (ME) Support Vector Machines (SVM) Interesting feature-creating mechanisms Framework for future analysis

Framework Movie Reviews Develop Features NB ME SVM Extract Insights Evaluate Results Training Model

Prior Work  Prior classification based on:  source/source style  genre  knowledge-based  semantic orientation

The Data  Internet Movie Database (IMDB) archive  Limited data to:  Reviews with author rating  Positive and negative reviews (no neutral)  19 positive, 19 negative reviews per author  Interim Dataset:  752 negative reviews  1301 positive reviews  144 reviewers represented  Final Dataset: 700 positive, 700 negative (uniform distribution)

Baseline  Crafted word lists using independent CS grad students  Positive vs. negative word count Positive ListNegative ListAccuracyTies Human 1dazzling, brilliant, phenomenal, excellent, fantastic suck, terrible, awful, unwatchable, hideous 58%75% Human 2gripping, mesmerizing, riveting, spectacular, cool, awesome, thrilling, badass, excellent, moving, exciting bad, clichéd, sucks, boring, stupid, slow 64%39%  Frequency counts (including test data)  Hand-picked words Positive ListNegative ListAccuracyTies Human 3 + stats love, wonderful, best, great, superb, still, beautiful bad, worst, stupid, waste, boring, ?, ! 69%16%

Features  Unigrams  appear once, twice, or thrice removed  added negation tags (not, didn’t, isn’t)  Bigrams  matched number of unigrams  no negation  Parts of Speech  Position within review  First quarter  Middle half  Last quarter

Models Naïve Bayes

Models Naïve Bayes Maximum Entropy

Models Naïve Bayes Maximum Entropy Support Vector Machines

Results Feature Type#Frequency / Presence NBMESVM (1)Unigrams > Freq.78.7N/A72.8 (2)Unigrams > Pres (3)Unigrams > 3 + Top Bigrams32330Pres (4)Top Bigrams16165Pres

Results Feature Type#Frequency / Presence NBMESVM (1)Unigrams > Freq.78.7N/A72.8 (2)Unigrams > Pres (3)Unigrams > 3 + Top Bigrams32330Pres (4)Top Bigrams16165Pres (5)Unigrams > 3 + POS16695Pres (6)Adjectives2633Pres (7)Top Unigrams2633Pres (8)Unigrams > 3 + Position22430Pres

Insights  SVM, but only 1-2% better  Not comparable to topic-based categorization models  Simple unigram presence the best  Presence > Frequency, not like topic-based  Uncovered “thwarted expectations” narrative  “Okay, I’m really ashamed of it, but I enjoyed it. I mean, I admit it’s a really awful movie.”

Future Work  Features that indicate sentences are on topic  Weighted by if related to overall film  “the whole is not necessarily the sum of the parts”  Important, because “thwarted- expectations” rhetoric present in many types of text  “This movie was wonderful, said no one ever.” – Don

Conclusion  Sentiment classification is a growing task, especially since 2002  Weighted sentence interesting idea  Our final project: movie scripts, anchored by reviews