Polarized Lexicon for Review Classification.
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TL;DR
This paper presents an approach to build polarized lexicon tables, and then applies these tables to the review polarity classification task, and results indicate a definite improvement in the classification accuracy.
Abstract
Customer reviews of products and services have positive or negative orientation depending on their experience. Determining the polarity of the reviews depend on the orientation of the words in the text, where some words are positive oriented and some are negative oriented. In this paper, we present an approach to build polarized lexicon tables, and then apply these tables to the review polarity classification task. Our results indicate a definite improvement in the classification accuracy when the polarized lexicon tables are used. 1