1. Sentiment Analyzer Using a Multi-Level Classifier
Tommy Tsai and Sam Yam
CS224N Final Project
Spring 2006

2. Motivation
Humans can infer a document’s sentiment orientation from high-level features
Sentiment of subjective sentences only
Local sentiments of select portions of the document
Length of document (domain-specific)
Domain of documents analyzed: 2000 IMDB movie reviews
1000 positive, 1000 negative (classified by hand)
Hard
Most mention both positive and negative aspects
Many are written in a sarcastic tone

3. Architecture Three stages Subjectivity filter
N-gram-based classifier
Input: Full review
Output: Filtered review
Sentence-level sentiment classifier
Also N-gram-based
Input: Filtered review
Output: Positive/negative classification and scores for each sentence
Document-level sentiment classifier
Support vector machine classifier
Inputs: Filtered review + scores and classification for each sentence
Output: Positive/negative classification for the entire review

4. Stage 3 SVM Classifier Support vector machines
Proven to have good performance for machine-learning classification problems
Maximization of “margin”: absolute distances between classification hyperplane and closest data points on either side
Features that worked well:
Average sentence-level “positive” and “negative” classification scores
Document-wide ratio of positive sentences to all sentences (PSRs)
Local PSRs for each of 5 buckets in the review
Number of sentences in the review

5. Results and Conclusions
Hybrid model worked well  our multi-level model does improve classifier’s discriminatory power
Character N-gram models worked better than token N-gram models
Negative reviews are harder to classify!
30 video game reviews: 27/30 = 90% accuracy