A Novel Approach for Dynamic Polarity Mining from Customer Reviews

The dynamic opinion words usually have different polarity directions when they are in combination with different features. Determining the polarity direction of these dynamic opinion words is one of the difficult problems in opinion mining. Although the opinion words with dynamic polarity are usually less than those with static polarity, these opinion words can be matched with most features, can appear very frequently in customer reviews. So the impact on the overall feature-opinion extraction accuracy and the calculation of comprehensive consumer word of mouth cannot be ignored. In this paper, we address this issue of judging the polarity direction of dynamic opinion words in different feature contexts by means of customer review mining and voting strategy. Our approach is based on this hypothesis: when the corpus scale is big enough, the word of mouth of product features are relatively stable. The experimental results verified the effectiveness of our method. Although the test is performed in mobile phone review areas, the approach can be easily applied to other areas.