Tracking Brand-Associated Polarity-Bearing Topics in User Reviews

TL;DR

This paper introduces a dynamic model that automatically detects and tracks brand-related sentiment and topics over time in customer reviews, improving brand reputation analysis.

Contribution

The proposed dBTM model uniquely combines Gaussian state space models with meta learning to track evolving brand sentiment and topics in review data.

Findings

Outperforms baseline models in brand ranking accuracy.

Achieves high topic coherence and distinctiveness.

Effectively captures polarity shifts over time.

Abstract

Monitoring online customer reviews is important for business organisations to measure customer satisfaction and better manage their reputations. In this paper, we propose a novel dynamic Brand-Topic Model (dBTM) which is able to automatically detect and track brand-associated sentiment scores and polarity-bearing topics from product reviews organised in temporally-ordered time intervals. dBTM models the evolution of the latent brand polarity scores and the topic-word distributions over time by Gaussian state space models. It also incorporates a meta learning strategy to control the update of the topic-word distribution in each time interval in order to ensure smooth topic transitions and better brand score predictions. It has been evaluated on a dataset constructed from MakeupAlley reviews and a hotel review dataset. Experimental results show that dBTM outperforms a number of competitive baselines in brand ranking, achieving a good balance of topic coherence and uniqueness, and extracting well-separated polarity-bearing topics across time intervals.