Measuring praise and criticism : inference of semantic orientation from association

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DOIResolve DOI: http://doi.org/10.1145/944012.944013
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TypeArticle
Journal titleACM Transactions on Information Systems (TOIS)
Volume21
Issue4
Pages315346; # of pages: 30
Subjectalgorithms; experimentation; semantic orientation; semantic association; web mining; text mining; text classification; unsupervised learning; mutual information; latent semantic analysis
AbstractThe evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., 'honest', 'intrepid') and negative semantic orientation indicates criticism (e.g., 'disturbing', 'superfluous'). Semantic orientation varies in both direction (positive or negative) and degree (mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems (chatbots). This paper introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8% on the full test set, but the accuracy rises above 95% when the algorithm is allowed to abstain from classifying mild words.
Publication date
LanguageEnglish
AffiliationNational Research Council Canada; NRC Institute for Information Technology
Peer reviewedNo
NRC number46516
NPARC number5210015
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Record identifier1b268840-85b3-4e70-af95-dc399b6cd2b4
Record created2008-12-02
Record modified2016-05-09
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