@inproceedings{argamon:ada,
        title = {{Automatically Determining Attitude Type and Force for Sentiment Analysis}},
        address = {Poznan, Poland},
        author = {S. Argamon and K. Bloom and A. Esuli and F. Sebastiani},
        booktitle = {Proceedings of the 3rd Language and Technology Conference (LTC'07)},
        pages = {369--373},
        year = 2007,
        biburl = {http://www.bibsonomy.org/bibtex/2e844ab698ddc8b5d827d7455c0d87f44/renew},
	keywords = {analysis sentiment}
}

@inproceedings{1218990,
        title = {A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts},
        address = {Morristown, NJ, USA},
        author = {Bo Pang and Lillian Lee},
        booktitle = {ACL '04: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics},
        pages = 271,
        publisher = {Association for Computational Linguistics},
        year = 2004,
        url = {http://portal.acm.org/citation.cfm?id=1218990},
        location = {Barcelona, Spain},
	doi = {http://dx.doi.org/10.3115/1218955.1218990},
	description = {A sentimental education},
	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.},
	biburl = {http://www.bibsonomy.org/bibtex/20b6f267021dde9c3181e88c5100a7552/renew},
	keywords = {analysis sentiment}
}