Building a Sentiment Summarizer for Local Service Reviews
Building a Sentiment Summarizer for Local Service Reviews
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发表时间：
2008
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通讯作者：
Sasha Blair-Goldensohn;K. Hannan;Ryan T. McDonald;T. Neylon;George A. Reis;Jeffrey C. Reynar
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作者：
Sasha Blair-Goldensohn;K. Hannan;Ryan T. McDonald;T. Neylon;George A. Reis;Jeffrey C. Reynar
在线用户评论正日益成为用于测量电子产品、餐馆、商家等的质量的事实上的标准。在线评论的绝对数量使得人类难以处理和提取所有有意义的信息以便进行有根据的购买。因此，已经出现了一种趋势，即系统可以自动地从一组评论中总结意见，并以易于处理的方式显示它们（1，9）。在本文中，我们提出了一个系统，总结了一个本地服务，如餐厅或酒店的意见。特别是，我们关注基于方面的摘要模型（8），其中摘要是通过提取服务的相关方面（如服务或价值），聚合每个方面的情感，并选择方面相关的文本来构建的。我们描述了我们的系统的方面提取和情感检测模块的细节。这些模型的一个新的方面是，他们利用用户提供的标签和领域的特定特征的服务评论，以提高质量。
Online user reviews are increasingly becoming the de-facto standard for measuring the quality of electronics, restau- rants, merchants, etc. The sheer volume of online reviews makes it difficult for a human to process and extract all meaningful information in order to make an educated pur- chase. As a result, there has been a trend toward systems that can automatically summarize opinions from a set of re- views and display them in an easy to process manner (1, 9). In this paper, we present a system that summarizes the sen- timent of reviews for a local service such as a restaurant or hotel. In particular we focus on aspect-based summarization models (8), where a summary is built by extracting relevant aspects of a service, such as service or value, aggregating the sentiment per aspect, and selecting aspect-relevant text. We describe the details of both the aspect extraction and sentiment detection modules of our system. A novel aspect of these models is that they exploit user provided labels and domain specific characteristics of service reviews to increase quality.