A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts Export

@inproceedings{citeulike:3281540, 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 efcient techniques for nding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints.}, author = {Pang, Bo and Lee, Lillian}, booktitle = {In Proceedings of the ACL}, citeulike-article-id = {3281540}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.9144}, keywords = {sentiment}, pages = {271--278}, posted-at = {2008-12-02 03:53:19}, priority = {2}, title = {A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.9144}, year = {2004} }

TY - CONF ID - citeulike:3281540 L3 - citeulike-article-id:3281540 N2 - 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 efcient techniques for nding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints. EP - 278 TI - A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts T2 - In Proceedings of the ACL SP - 271 KW - sentiment AU - Pang, Bo AU - Lee, Lillian PY - 2004/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.9144 ER -