Great Food, Lousy Service Topic Modeling for

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Great Food, Lousy Service Topic Modeling for Sentiment Analysis in Sparse Reviews Robin Melnick rmelnickstanford.edu Dan Preston dprestonstanford.edu OpenTable.com Short Characters Words Sparse An unexpected combination of Left-Bank

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Great Food, Lousy Service Topic Modeling for Sentiment Analysis in Sparse Reviews Robin Melnick
rmelnick@stanford.edu Dan Preston
dpreston@stanford.edu

OpenTable.com

Short Characters Words

Sparse “An unexpected combination of Left-Bank Paris and Lower Manhattan in Omaha. Divine. Inspirational and a great value.”

Food?
Ambiance?
Service?
Noise?

Skewed

Correlations

SVM + Features, Features, Features! 30+ preprocessing and SVM classification features,
~50 configurations

Key Features Stemming
Porter 1980 via NLTK
<fast>, <faster>, <fastest>  <fast>
Negation processing
(enhanced approach from Pang et al. 2002)
“Not a great experience.”  NOT_great
“They never disappoint!”  NOT_disappoint
Net sentiment count
pos/neg lexicon (Harvard General Inquirer)
running +/- count
“Incredible(+) food, but our server was rude(-).”  (0)

Results (so far) Trained on 10,000 reviews
Tested on ~80,000 reviews

Accuracy
Baseline: 50.0%
Intermediate model: 56.6% (1.13x)

abs( average scoring delta ): 0.56

Topic Modeling Hand-seeded topic-word list expanded via WordNet SynSets

sub-topic classifiers
topic-filtered n-grams
<soupFOOD was fantasticADJ>
<fantasticADJ soupFOOD was>
topic-word proximity filtering
both above  <fantasticADJ/FOOD>.

Results:

Word-Rating Distributions “worst” “mediocre” “decent” “solid” “exceeded”

Frequency-Weighted Entropy Model Accuracy
Baseline: 50.0%
Intermediate model: 56.6%
Best (entropy) model: 58.6% (1.17x)

abs( average scoring delta ): 0.56  0.52

Great Food, Lousy Service Topic Modeling for

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