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Sentiment analysis has been a helpful mechanism that targets to understand the market feedback on certain commodities by utilizing the user comments. In the process of providing comments, each user comment is generated based on his/her preference which is referred to as harshness. Existing methods mainly apply majority voting or its variants to directly infer the evaluation of products. Nevertheless, due to the ignorance of the harshness of users, these methods will lead to low-quality inference outcome of sentiment analysis, which is far from the result of the expert analysis report. To this end, we propose HARK, a harshness-aware product analysis framework. First, we employ a Bayesian-based model for sentiment analysis. Moreover, in order to infer the reliable sentiment concerning each product from all the comments, we present a probabilistic graphical model in which the harshness is incorporated. Extensive experimental evaluations have shown that the result of our method is more consistent with the expert evaluation than that of the state-of-the-art methods. And our method also outperforms the method which infers the final sentiment with the ground truth of comments but without involving the harshness of users.

HARK: Harshness-Aware Sentiment Analysis Framework for Product Review (Student Abstract)