"2321","Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews","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.   ","http://cogprints.org/2321/","Turney, Peter D.","UNSPECIFIED","       <span class=\"person_name\">Turney, Peter D.</span>     (2002)  <a href=\"http://cogprints.org/2321/\"><em>Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews.</em></a>       [Conference Paper]           ","","2002"