@misc{cogprints2321,
           title = {Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews},
          author = {Peter D. Turney},
            year = {2002},
           pages = {417--424},
             url = {http://cogprints.org/2321/},
        abstract = {This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., "subtle nuances") and a negative semantic orientation when it has bad associations (e.g., "very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word "excellent" minus the mutual information between the given phrase and the word "poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74\% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84\% for automobile reviews to 66\% for movie reviews. 

}
}

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