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    <titleInfo>
        <title>Tracking Brand-Associated Polarity-Bearing Topics in User Reviews</title>
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    <name type="personal">
        <namePart type="given">Runcong</namePart>
        <namePart type="family">Zhao</namePart>
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        <namePart type="family">Gui</namePart>
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    <name type="personal">
        <namePart type="given">Hanqi</namePart>
        <namePart type="family">Yan</namePart>
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    <name type="personal">
        <namePart type="given">Yulan</namePart>
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    <abstract>Monitoring online customer reviews is important 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 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.1</abstract>
    <identifier type="citekey">zhao-etal-2023-tracking</identifier>
    <identifier type="doi">10.1162/tacl_a_00555</identifier>
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        <date>2023</date>
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%0 Journal Article
%T Tracking Brand-Associated Polarity-Bearing Topics in User Reviews
%A Zhao, Runcong
%A Gui, Lin
%A Yan, Hanqi
%A He, Yulan
%J Transactions of the Association for Computational Linguistics
%D 2023
%V 11
%I MIT Press
%C Cambridge, MA
%F zhao-etal-2023-tracking
%X Monitoring online customer reviews is important 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 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.1
%R 10.1162/tacl_a_00555
%U https://aclanthology.org/2023.tacl-1.24
%U https://doi.org/10.1162/tacl_a_00555
%P 404-418

Markdown (Informal)

[Tracking Brand-Associated Polarity-Bearing Topics in User Reviews](https://aclanthology.org/2023.tacl-1.24) (Zhao et al., TACL 2023)

• Tracking Brand-Associated Polarity-Bearing Topics in User Reviews (Zhao et al., TACL 2023)

ACL

• Runcong Zhao, Lin Gui, Hanqi Yan, and Yulan He. 2023. Tracking Brand-Associated Polarity-Bearing Topics in User Reviews. Transactions of the Association for Computational Linguistics, 11:404–418.