A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
<|reference_start|>A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts: 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.<|reference_end|>
@article{pang2004a,
    title={A Sentimental Education: Sentiment Analysis Using Subjectivity
  Summarization Based on Minimum Cuts},
    author={Bo Pang and Lillian Lee},
    journal={Proceedings of the 42nd ACL, pp. 271--278, 2004},
    year={2004},
    archivePrefix={arXiv},
    eprint={cs/0409058},
    primaryClass={cs.CL}
}