Buy it - Don't buy it: Sentiment classification on amazon reviews using sentence polarity shift

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Conference Proceeding

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)


In recent years, sentiment classification has been an appealing task for so many reasons. However, the subtle manner in which people write reviews has made achieving high accuracy more challenging. In this paper, we investigate the improvements on sentiment classification baselines using sentiment polarity shift in reviews. We focus on Amazon online reviews for different types of product. First, we use our newly-proposed Sentence Polarity Shift (SPS) algorithm on review documents, reducing the relative classification loss due to inconsistent sentiment polarities within reviews by an average of 16% over a supervised sentiment classifier. Second, we build up on a popular supervised sentiment classification baseline by adding different features which provide better improvement over the original baseline. The improvement shown by this technique suggests modeling sentiment classification systems based on polarity shift combined with sentence and document-level features. © 2012 Springer-Verlag.

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