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.
Data: http://www.cs.cornell.edu/people/pabo/movie-review-data/
Press mentions:
BibTeX entry:
@InProceedings{Pang+Lee:04a,
author = {Bo Pang and Lillian Lee},
title = {A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts},
booktitle = "Proceedings of the ACL",
pages = {271--278},
year = 2004
}