Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10355055 | Information Processing & Management | 2016 | 10 Pages |
Abstract
The polarity shift problem is a major factor that affects classification performance of machine-learning-based sentiment analysis systems. In this paper, we propose a three-stage cascade model to address the polarity shift problem in the context of document-level sentiment classification. We first split each document into a set of subsentences and build a hybrid model that employs rules and statistical methods to detect explicit and implicit polarity shifts, respectively. Secondly, we propose a polarity shift elimination method, to remove polarity shift in negations. Finally, we train base classifiers on training subsets divided by different types of polarity shifts, and use a weighted combination of the component classifiers for sentiment classification. The results on a range of experiments illustrate that our approach significantly outperforms several alternative methods for polarity shift detection and elimination.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Rui Xia, Feng Xu, Jianfei Yu, Yong Qi, Erik Cambria,