Aspect Sentiment Model for Micro Reviews
Aspect Sentiment Model for Micro Reviews
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DOI：
10.1109/icdm.2017.83
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发表时间：
2017-11
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影响因子：
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通讯作者：
Reinald Kim Amplayo;Seung-won Hwang
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文献类型：
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作者：
Reinald Kim Amplayo;Seung-won Hwang
本文针对面向微观评论的基于方面的情感分析(ABSA)，提出了一种面向方面的情感模型。这项任务对于理解大多数用户撰写的短评论非常重要，而现有的主题模型则针对具有足够的共现模式的专家级长评论。由于元数据缺失、主题异构性和冷启动问题，目前使用元数据信息聚合微观评论的方法可能也不是很有效。为此，我们提出了一个称为微观方面情感模型(MicroASM)的模型。MicroASM是基于这样的观察：1)短评论被视为信息的构建块，情感-体词对被视为信息的构建块，2)可以聚集成更大的评论。实验表明，与现有的方面情感模型相比，该模型在特征词提取等方面的任务和情感分类等文档级任务上具有更好的性能。
This paper aims at an aspect sentiment model for aspect-based sentiment analysis (ABSA) focused on micro reviews. This task is important in order to understand short reviews majority of the users write, while existing topic models are targeted for expert-level long reviews with sufficient co-occurrence patterns to observe. Current methods on aggregating micro reviews using metadata information may not be effective as well due to metadata absence, topical heterogeneity, and cold start problems. To this end, we propose a model called Micro Aspect Sentiment Model (MicroASM). MicroASM is based on the observation that short reviews 1) are viewed with sentiment-aspect word pairs as building blocks of information, and 2) can be clustered into larger reviews. When compared to the current state-of-the-art aspect sentiment models, experiments show that our model provides better performance on aspect-level tasks such as aspect term extraction and document-level tasks such as sentiment classification.