Confidence-Aware Sentiment Quantification via Sentiment Perturbation Modeling

Sentiment Quantification aims to detect the overall sentiment polarity of users from a set of reviews corresponding to a target. Existing methods equally treat and aggregate individual reviews’ sentiment to judge the overall sentiment polarity. However, the confidence of each review is not equal in sentiment quantification where sentiment perturbation arising from high- and low-confidence reviews may degrade the accuracy of Sentiment Quantification. Specifically, fake reviews with deceptive sentiments are low confidence, which perturbs the overall sentiment prediction. Whereas, some reviews generated by responsible users are high confidence. They contain authoritative suggestions so they should be emphasized in Sentiment Quantification. In this paper, we design and build COSE, a confidence-aware sentiment quantification framework, which can measure the confidence of individual reviews to eliminate sentiment perturbation and facilitate sentiment quantification. We design a Review Graph that achieves review confidence modeling in an unsupervised manner and obtains review confidence representations. Moreover, we develop a dynamic fusion attention mechanism, which produces sentiment “de-perturbation” vectors to eliminate the sentiment perturbation based on the confidence representations. Extensive experiments on large-scale review datasets validate the significant superiority of COSE over the state-of-the-art.

I. Introduction

Sentiment analysis tasks automatically detect users’ sentiment polarity (positive or negative) on a given target (e.g., products, services, issues, and events), which has been well studied by many works for individual review analysis [1], [2], [3]. Instead of individual review sentiments, many sentiment classification applications demand the attention of overall sentiment polarities on a set of reviews corresponding to a target, such as market research for products, brand positioning, and open-answer classification for social sciences [4]. Sentiment Quantification aims to detect the overall sentiment polarity of users from a set of reviews corresponding to a specific target [5], [6].