Aspect Extraction with Automated Prior Knowledge Learning
Aspect Extraction with Automated Prior Knowledge Learning
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DOI：
10.3115/v1/p14-1033
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Zhiyuan Chen;Arjun Mukherjee;B. Liu
中科院分区：
文献类型：
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作者：
Zhiyuan Chen;Arjun Mukherjee;B. Liu
情感特征提取是情感分析中的一项重要任务。主题建模是一种流行的任务方法。然而，无监督的主题模型经常产生不连贯的方面。为了解决这个问题，已经提出了几个基于知识的模型来结合用户提供的先验知识来指导建模。在这篇文章中，我们向前迈出了一大步，并展示了在大数据时代，在没有任何用户输入的情况下，可以从网络上可用的大量评论数据中自动学习先验知识。然后，主题模型可以使用这些知识来发现更连贯的方面。有两个关键挑战：(1)从不同领域的评论中学习高质量的知识；(2)使模型具有容错能力，以处理可能错误的知识。提出了一种新的方法来解决这些问题。使用36个领域的评论进行的实验结果表明，该方法在最新的基线上取得了显著的改进。
Aspect extraction is an important task in sentiment analysis. Topic modeling is a popular method for the task. However, unsupervised topic models often generate incoherent aspects. To address the issue, several knowledge-based models have been proposed to incorporate prior knowledge provided by the user to guide modeling. In this paper, we take a major step forward and show that in the big data era, without any user input, it is possible to learn prior knowledge automatically from a large amount of review data available on the Web. Such knowledge can then be used by a topic model to discover more coherent aspects. There are two key challenges: (1) learning quality knowledge from reviews of diverse domains, and (2) making the model fault-tolerant to handle possibly wrong knowledge. A novel approach is proposed to solve these problems. Experimental results using reviews from 36 domains show that the proposed approach achieves significant improvements over state-of-the-art baselines.