Polarity shift detection,elimination and ensemble: A three-stage model for document-level sentiment analysis |
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| Institution: | 1. RMIT Department of Computer Science and NICTA, Australia;2. MedWhat.com and the University of Melbourne, Australia |
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| 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. |
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| Keywords: | Sentiment analysis Sentiment classification Polarity shift |
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