"2322","Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus","The evaluative character of a word is called its semantic orientation. A positive semantic orientation implies desirability (e.g., \"honest\", \"intrepid\") and a negative semantic orientation implies undesirability (e.g., \"disturbing\", \"superfluous\"). This paper introduces a simple algorithm for unsupervised learning of semantic orientation from extremely large corpora. The method involves issuing queries to a Web search engine and using pointwise mutual information to analyse the results. The algorithm is empirically evaluated using a training corpus of approximately one hundred billion words — the subset of the Web that is indexed by the chosen search engine. Tested with 3,596 words (1,614 positive and 1,982 negative), the algorithm attains an accuracy of 80%. The 3,596 test words include adjectives, adverbs, nouns, and verbs. The accuracy is comparable with the results achieved by Hatzivassiloglou and McKeown (1997), using a complex four-stage supervised learning algorithm that is restricted to determining the semantic orientation of adjectives. ","http://cogprints.org/2322/","Turney, Peter D. and Littman, Michael L.","UNSPECIFIED","       <span class=\"person_name\">Turney, Peter D.</span> and <span class=\"person_name\">Littman, Michael L.</span>     (2002)  <a href=\"http://cogprints.org/2322/\"><em>Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus.</em></a>       [Departmental Technical Report]         (Unpublished)   ","","2002"