1. Review-Level Sentiment Classification with Sentence-Level Polarity Correction
- Author
-
Orimaye, Sylvester Olubolu, Alhashmi, Saadat M., Siew, Eu-Gene, and Kang, Sang Jung
- Subjects
Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computer Science - Learning - Abstract
We propose an effective technique to solving review-level sentiment classification problem by using sentence-level polarity correction. Our polarity correction technique takes into account the consistency of the polarities (positive and negative) of sentences within each product review before performing the actual machine learning task. While sentences with inconsistent polarities are removed, sentences with consistent polarities are used to learn state-of-the-art classifiers. The technique achieved better results on different types of products reviews and outperforms baseline models without the correction technique. Experimental results show an average of 82% F-measure on four different product review domains., Comment: 15 pages. This paper is based on the same sentence-level technique proposed in Orimaye, S. O., Alhashmi, S. M., and Siew, E. G. Buy it-dont buy it: sentiment classification on Amazon reviews using sentence polarity shift. In PRICAI 2012: Trends in Artificial Intelligence, pp. 386-399. Springer Berlin Heidelberg
- Published
- 2015