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dc.titleLanguage-independent sentiment analysis using subjectivity and positional information
dc.contributor.authorRaychev, V.
dc.contributor.authorNakov, P.
dc.identifier.citationRaychev, V.,Nakov, P. (2009). Language-independent sentiment analysis using subjectivity and positional information. International Conference Recent Advances in Natural Language Processing, RANLP : 360-364. ScholarBank@NUS Repository.
dc.description.abstractWe describe a novel language-independent approach to the task of determining the polarity, positive or negative, of the author's opinion on a specific topic in natural language text. In particular, weights are assigned to attributes, individual words or word bi-grams, based on their position and on their likelihood of being subjective. The subjectivity of each attribute is estimated in a two-step process, where first the probability of being subjective is calculated for each sentence containing the attribute, and then these probabilities are used to alter the attribute's weights for polarity classification. The evaluation results on a standard dataset of movie reviews shows 89.85% classification accuracy, which rivals the best previously published results for this dataset for systems that use no additional linguistic information nor external resources.
dc.subjectPolarity classification
dc.subjectSentiment analysis
dc.subjectSubjectivity identification
dc.subjectText categorization
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleInternational Conference Recent Advances in Natural Language Processing, RANLP
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