Abstract
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.
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Orimaye, S.O., Alhashmi, S.M., Siew, EG. (2012). Buy It - Don’t Buy It: Sentiment Classification on Amazon Reviews Using Sentence Polarity Shift. In: Anthony, P., Ishizuka, M., Lukose, D. (eds) PRICAI 2012: Trends in Artificial Intelligence. PRICAI 2012. Lecture Notes in Computer Science(), vol 7458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32695-0_35
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DOI: https://doi.org/10.1007/978-3-642-32695-0_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32694-3
Online ISBN: 978-3-642-32695-0
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