Paper: Improvement of a Naive Bayes Sentiment Classifier Using MRS-Based Features

ACL ID S14-1003
Title Improvement of a Naive Bayes Sentiment Classifier Using MRS-Based Features
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

This study explores the potential of us- ing deep semantic features to improve bi- nary sentiment classification of paragraph- length movie reviews from the IMBD website. Using a Naive Bayes classifier as a baseline, we show that features extracted from Minimal Recursion Semantics repre- sentations in conjunction with back-off re- placement of sentiment terms is effective in obtaining moderate increases in accu- racy over the baseline?s n-gram features. Although our results are mixed, our most successful feature combination achieves an accuracy of 89.09%, which represents an increase of 0.76% over the baseline per- formance and a 6.48% reduction in error.