  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.
