A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
Computation and Language
2007-05-23 v1
Abstract
Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as "thumbs up" or "thumbs down". To determine this sentiment polarity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints.
Cite
@article{arxiv.cs/0409058,
title = {A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts},
author = {Bo Pang and Lillian Lee},
journal= {arXiv preprint arXiv:cs/0409058},
year = {2007}
}
Comments
Data available at http://www.cs.cornell.edu/people/pabo/movie-review-data/