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Tracking Brand-Associated Polarity-Bearing Topics in User Reviews

arXiv 2301.07183 ↗First posted 3 Jan 2023cs.IR cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 39 references that are in the map. First posted on arXiv 3 Jan 2023.

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References · 19

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7 more
  1. 2021Topic-Guided Abstractive Multi-Document Summarizationcited by 19
  2. 2020Personalized Adaptive Meta Learning for Cold-start User Preference Predictioncited by 15
  3. 2020HotelRec: a Novel Very Large-Scale Hotel Recommendation Datasetcited by 14
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  6. 2021TOUR: Dynamic Topic and Sentiment Analysis of User Reviews for Assisting App Releasecited by 2
  7. 2021Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviewscited by 1

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