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首页> 外文期刊>Multimedia, IEEE Transactions on >Word-of-Mouth Understanding: Entity-Centric Multimodal Aspect-Opinion Mining in Social Media
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Word-of-Mouth Understanding: Entity-Centric Multimodal Aspect-Opinion Mining in Social Media

机译：口碑理解：社交媒体中以实体为中心的多模式方面观点挖掘

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摘要

Most existing approaches on aspect-opinion mining focus on the text domain and cannot be applied to social media where the aspects are essentially multimodal and the opinions depend on the specific aspects. To address the problem of multimodal aspect-opinion mining for entities by leveraging multiple cross-collection sources in social media, in this paper we propose a multimodal aspect-opinion model (mmAOM) considering both user-generated photos and textual documents to simultaneously capture correlations between textual and visual modalities, as well as associations between aspects and opinions . By identifying the aspects and the corresponding opinions related to entities, we apply the mmAOM to entity association visualization and multimodal aspect-opinion retrieval. We have conducted extensive experiments on real-world datasets of entities including Flickr photos, Tripadvisor reviews, and news articles. Qualitative and quantitative evaluation results have validated the effectiveness of the multimodal aspect-opinion mining model, and demonstrated the utility of the derived aspects and opinions from mmAOM in applications of entity association visualization and aspect-opinion retrieval.
机译：现有的关于方面观点挖掘的大多数方法都集中在文本域上，不能应用于方面本质上是多模式的并且意见取决于具体方面的社交媒体。为了通过利用社交媒体中的多个交叉收集源来解决实体的多模式方面意见挖掘问题，在本文中，我们提出了一种多模式方面意见模型（mmAOM），该模型同时考虑了用户生成的照片和文本文档以同时捕获相关性在文本和视觉方式之间，以及方面和观点之间的关联。通过识别方面和与实体相关的相应意见，我们将mmAOM应用到实体关联可视化和多模式方面意见检索中。我们对实体的真实世界数据集进行了广泛的实验，包括Flickr照片，Tripadvisor评论和新闻文章。定性和定量评估结果验证了多模式方面意见挖掘模型的有效性，并证明了mmAOM派生的方面和意见在实体关联可视化和方面意见检索中的应用。

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