Tracking Brand-Associated Polarity-Bearing Topics in User Reviews

Tracking Brand-Associated Polarity-Bearing Topics in User Reviews Runcong Zhao1,2, Lin Gui1, Hanqi Yan2, Yulan He1,2,3 1King’s College London, United Kingdom, 2University of Warwick, United Kingdom, 3The Alan Turing Institute, United Kingdom runcong.zhao@warwick.ac.uk, yulan.he@kcl.ac.uk Abstract Monitoring online customer reviews is im- portant for business organizations 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 organized in temporally ordered time intervals. dBTM models the evolution of the latent brand po- larity 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 distri- bution in each time interval in order to ensure smooth topic transitions and better brand score predictions. It has been evaluated on a data- set constructed from MakeupAlley reviews and a hotel review dataset. Experimental re- sults 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.1 1 Introduction With the increasing popularity of social media platforms, customers tend to share their personal experience towards products online. Tracking cus- tomer reviews online could help business orga- nizations to measure customer satisfaction and better manage their reputations. Monitoring brand- associated topic changes in reviews can be done through the use of dynamic topic models (Blei and Lafferty, 2006; Wang et al., 2008; Dieng et al., 2019). Approaches such as the dynamic Joint Sentiment-Topic (dJST) model (He et al., 2014) are able to extract polarity-bearing topics evolved over time by assuming the dependency of the sentiment-topic-word distributions across time 1Data and code are available at https://github .com/BLPXSPG/dBTM. slices. They require the incorporation of word prior polarity information, however, and assume topics are associated with discrete polarity cat- egories. Furthermore, they are not able to infer brand polarity scores directly. A recently proposed Brand-Topic Model (BTM) (Zhao et al., 2021) is able to automat- ically infer real-valued brand-associated senti- ment scores from reviews and generate a set of sentiment-topics by gradually varying its associ- ated sentiment scores from negative to positive. This allows users to detect, for example, strongly positive topics or slightly negative topics. BTM, however, assumes all documents are available prior to model learning and cannot track topic evolution and brand polarity changes over time. In this paper, we propose a novel framework inspired by Meta-Learning, which is widely used for distribution adaptation tasks (Suo et al., 2020). When training the model on temporally ordered documents divided into time slice, we assume that extracting polarity-bearing topics and infer- ring brand polarity scores in each time slice can be treated as a new sub-task and the goal of model learning is to learn to adapt the topic-word dis- tributions associated with different brand polarity scores in a new time slice. We use BTM as the base model …

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