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<p>Brand competitive analysis is crucial for businesses to understand their market position and develop strategies to outperform competitors. As consumer behavior and market trends evolve, companies increasingly rely on data-driven insights to stay ahead. In this context, online reviews on E-commerce platforms offer a wealth of information, providing valuable opportunities for analyzing consumer preferences. With the rise of multimodal reviews (combining both text and images), it has become essential to harness these diverse data sources effectively. To address this need, we first propose a multimodal and multi-corpora latent Dirichlet allocation (MM-LDA) model for static data. It uncovers both general topics and brand-specific topics, enabling fine-grained insights into consumer concerns and brand positioning. We further extend MM-LDA into a dynamic framework and propose the multimodal and multi-corpora dynamic topic model (MM-DTM), which incorporates temporal dynamics to capture topic evolution over time. The effectiveness of our approaches are demonstrated using two datasets from ZOL and Baidu Tieba discussions.</p>

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Multimodal topic models for brand competitive analysis

  • 期刊论文

  • 发表日期：

    2026年1月16日

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  • Xiaoning Wang,
  • Xiaoling Lu,
  • Yuxuan Guo,
  • Li’ao Zhu,
  • Feifei Wang

摘要

Brand competitive analysis is crucial for businesses to understand their market position and develop strategies to outperform competitors. As consumer behavior and market trends evolve, companies increasingly rely on data-driven insights to stay ahead. In this context, online reviews on E-commerce platforms offer a wealth of information, providing valuable opportunities for analyzing consumer preferences. With the rise of multimodal reviews (combining both text and images), it has become essential to harness these diverse data sources effectively. To address this need, we first propose a multimodal and multi-corpora latent Dirichlet allocation (MM-LDA) model for static data. It uncovers both general topics and brand-specific topics, enabling fine-grained insights into consumer concerns and brand positioning. We further extend MM-LDA into a dynamic framework and propose the multimodal and multi-corpora dynamic topic model (MM-DTM), which incorporates temporal dynamics to capture topic evolution over time. The effectiveness of our approaches are demonstrated using two datasets from ZOL and Baidu Tieba discussions.