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An approach to rank reviews by fusing and mining opinions based on review pertinence

机译：一种基于评论相关性通过融合和挖掘观点对评论进行排名的方法

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摘要

Fusing and mining opinions from reviews posted in webs or social networks is becoming a popular research topic in recent years in order to analyze public opinions on a specific topic or product. Existing research has been focused on extraction, classification and summarization of opinions from reviews in news websites, forums and blogs. An important issue that has not been well studied is the degree of relevance between a review and its corresponding article. Prior work simply divides reviews into two classes: spam and non-spam, neglecting that the non-spam reviews could have different degrees of relevance to the article. In this paper, we propose a notion of "Review Pertinence" to study the degree of this relevance. Unlike usual methods, we measure the pertinence of review by considering not only the similarity between a review and its corresponding article, but also the correlation among reviews. Experiment results based on real data sets collected from a number of popular portal sites show the obvious effectiveness of our method in ranking reviews based on their pertinence, compared with three baseline methods. Thus, our method can be applied to efficiently retrieve reviews for opinion fusion and mining and filter review spam in practice. (C) 2014 Elsevier B.V. All rights reserved.
机译：为了分析针对特定主题或产品的公众意见，近年来融合和挖掘来自发布在网络或社交网络中的评论的观点已成为一种流行的研究主题。现有研究一直集中于从新闻网站，论坛和博客中的评论中提取，分类和总结观点。尚未对其进行深入研究的一个重要问题是评论与其相应文章之间的相关程度。先前的工作仅将评论分为两类：垃圾邮件和非垃圾邮件，而忽略了非垃圾邮件评论与文章的相关程度不同。在本文中，我们提出了“审查相关性”的概念来研究这种相关性的程度。与通常的方法不同，我们不仅通过考虑评论与其相应文章之间的相似性，还考虑评论之间的相关性来衡量评论的针对性。基于从许多受欢迎的门户网站收集的真实数据集的实验结果表明，与三种基准方法相比，我们的方法在根据其相关性对评论进行排名方面具有明显的有效性。因此，我们的方法可用于有效地检索评论以进行意见融合和挖掘，并在实践中过滤掉评论垃圾邮件。 （C）2014 Elsevier B.V.保留所有权利。

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