Amazon review utility estimator. Overview  Goal: To determine the “usefulness” of Amazon.com reviews  Using Mallet classifiers  Several custom features.

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Presentation transcript:

Amazon review utility estimator

Overview  Goal: To determine the “usefulness” of Amazon.com reviews  Using Mallet classifiers  Several custom features  If accurate, this system could be applied beyond Amazon, including other product reviews or even Slashdot/Digg comments.

Reviews  Used Amazon ECS: Collected large number of reviews over 4 categories: Textbooks, Digital Cameras, Music, DVD  Textbooks: 24,419 reviews with over 5 votes  Digital Cameras: 22,566  Music: 43,328  DVD: 132,208

Regression?  All of the length features seem to have a trend when grouped in buckets  DVD data Avg TotalAvg WordAvg Para 0-25% % % %

Regression  R 2 ~.3 Rating # of words

Regression Rating Avg Sentence Length

Features  Bag of words  Average: length, sentence length, word length  % of words that are stop words  # of spelling errors  # of paragraphs  Pronouns, articles, Proper nouns etc.  Punctuation  History

Stuff We Learned  Some good reviews are hard to find “e-toys has this for 19.99” rated helpful by 17/21 people.  And some people are just stupid “and there you have it. That's the secret. ” 77%... “On DVD, I'll buy this NOW! Not on VHS...Jezus...” 78%...  We attempted manually classifying ~100 reviews In 4 buckets around 30% accuracy In 2 buckets around 55%.... abstract.cs.washington.edu/~kylej1/quiz.php

Cont.  Trade off between Precision and Recall: Many features increase precision but hurt recall The range of good reviews is very broad  Word Count / Sentence Length / % stopwords have biggest impact Precision +5%, Recall -8%  Diminishing returns..

Cont.  Precision in the High 80s with the right combination of features Recall suffers, drops to between 40-50%  Experimenting with multiple classifiers in series. To boost recall without destroying precision Similar to Boosting.

Future  When should computer override customer rating? Amazon has huge # of “Labeled” data…but the labels are sometimes poor Review Quality is very subjective Weight based on # of total votes? ○ Some concerns with this  Bias detection Positive or Negative impact?

End  Questions?