Building Phrase Polarity Lexicons for Sentiment Analysis

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Abstract
Many approaches to sentiment analysis benefit from polarity lexicons. Most polarity lexicons include a list of polar (positive/negative) words, and sentiment analysis systems attempt to capture the occurrence of those words in text using polarity lexicons. Although there exist some polarity lexicons in many natural languages, most languages suffer from the lack of phrase polarity lexicons. Phrases play an important role in sentiment analysis because the polarity of a phrase cannot always be estimated based on the polarity of its parts. In this work, a hybrid approach is proposed for building phrase polarity lexicons which is experimented on Turkish as a low-resource language. The obtained classification accuracies in extracting and classifying phrases as positive, negative, or neutral, approve the effectiveness of the proposed methodology.
Year of Publication
2018
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
5
Issue
Regular Issue
Number
3
Number of Pages
98-105
Date Published
12/2018
ISSN Number
1989-1660
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