Exploiting Temporal Dynamics in Product Reviews for Dynamic Sentiment Prediction at the Aspect Level

• 1、Department of Computer Science and technology, Hunan University, Changsha 410082

Abstract：Online reviews and ratings play an important role in shaping the purchase decisions of customers in e-commerce. Many researches have been done to make proper recommendations for users, by exploiting reviews, ratings, user profiles, or behaviors. However, the dynamic evolution of user preferences and item properties haven't been fully exploited. Moreover, it lacks fine-grained studies at the aspect level. To address the above issues, this paper defines two concepts of user maturity and item popularity, to better explore the dynamic changes for users and items. This work strives to exploit fine-grained information at the aspect level and the evolution of users and items, for dynamic sentiment prediction. First, this paper analyze three real datasets from both the overall-level and the aspect-level, to discover the dynamic changes (i.e., gradual changes and sudden changes) in user aspect preferences and item aspect attributes. Next, propose a novel model of Aspect-based Sentiment Dynamic Prediction (ASDP), to dynamically capture and exploit the change patterns with uniform time intervals. this paper further proposes the improved model ASDP+ with a bin segmentation algorithm to set the time intervals non-uniformly based on the sudden changes. Experimental results on three real-world datasets show that the proposed model leads to significant improvements.

Keywords： artificial intelligence, review mining, opinion evolution, aspect level, temporal dynamics, sentiment prediction