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.