Opinion Searching in Multi-Product Reviews
Opinion Searching in Multi-Product Reviews
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DOI：
10.1109/cit.2006.132
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发表时间：
2006-09
期刊：
影响因子：
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通讯作者：
Jian Liu;Gengfeng Wu;Jianxin Yao
中科院分区：
文献类型：
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作者：
Jian Liu;Gengfeng Wu;Jianxin Yao
人们在购买前浏览网页查看产品评论已变得越来越普遍。然而，检索与客户需求相关的意见仍然具有挑战性。本文研究了意见搜索问题，其目的是在多产品评论中搜索与特定产品的特定特征相关的意见，并将其定位。我们的解决方案包括两个步骤：意见索引和意见检索。意见索引是识别意见片段并生成意见元组（产品，特征和情感）。意见检索就是在文档中查找与用户检索兴趣相匹配的意见元组，帮助用户在文档中定位相应的意见片段。从根本上说，意见索引应该能够识别功能-产品依赖关系（即，在评论文本的某处提到的特征在语义上与哪个产品相关联）。我们探索用机器学习技术来解决这个问题。
It is becoming common that people browse Web for product reviews before purchasing. However, to retrieve opinions relevant to customer desire still remains challenging. In this paper, we studied the problem of opinion searching, whose aim is to search the opinions about specific feature of specific product and locate them in multi-product reviews. Our solution includes two steps: opinion indexing and opinion retrieving. Opinion indexing is to identify opinion fragments and generate opinion tuples (product,feature and sentiment). Opinion retrieving is to look up the opinion tuples matching users' retrieving interests, and help users to locate the corresponding opinion fragments in documents. Fundamentally, opinion indexing should be able to identify the feature-product dependencies (i.e., a feature mentioned in somewhere of reviewing text is semantically associated with which product). We explore to resolve the problem with machine-learning techniques.