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Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus

Turney, Peter D. and Littman, Michael L. (2002) Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus. [Departmental Technical Report] (Unpublished)

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Abstract

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.

Item Type:Departmental Technical Report
Subjects:Computer Science > Artificial Intelligence
Computer Science > Language
Computer Science > Machine Learning
Computer Science > Statistical Models
ID Code:2322
Deposited By:Turney, Peter
Deposited On:15 Jul 2002
Last Modified:11 Mar 2011 08:54

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