Good location, terrible food: detecting feature sentiment in user-generated reviews
Good location, terrible food: detecting feature sentiment in user-generated reviews
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DOI：
10.1007/s13278-013-0119-7
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发表时间：
2013-06
影响因子：
2.8
通讯作者：
Mario Cataldi;Andrea Ballatore;Ilaria Tiddi;Marie-Aude Aufaure
中科院分区：
文献类型：
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作者：
Mario Cataldi;Andrea Ballatore;Ilaria Tiddi;Marie-Aude Aufaure
非专家每天在社交网络和博客上生成越来越多的在线非正式评论，涉及范围无限的产品和服务。用户不仅表达整体意见，而且经常关注他们感兴趣的特定功能。在功能级别自动理解“人们在想什么”可以极大地支持消费者和生产者的决策制定。在本文中，我们提出了一种将自然语言处理与统计技术相结合的特征级情感检测方法，以便从用户生成的评论中提取用户对产品和服务特定特征的意见。首先，我们提取领域特征，并将每个评论建模为词汇依赖图。其次，对于每次回顾，我们通过利用术语之间的句法依赖来估计相对于特征的极性。该方法是根据一组由用户生成的评论组成的基本事实进行评估的，这些评论由39名人类受试者手动注释，并在线提供，显示出它具有类似人类的捕捉特征级意见的能力。
A growing corpus of online informal reviews is generated every day by non-experts, on social networks and blogs, about an unlimited range of products and services. Users do not only express holistic opinions, but often focus on specific features of their interest. The automatic understanding of “what people think” at the feature level can greatly support decision making, both for consumers and producers. In this paper, we present an approach to feature-level sentiment detection that integrates natural language processing with statistical techniques, in order to extract users’ opinions about specific features of products and services from user-generated reviews. First, we extract domain features, and each review is modelled as a lexical dependency graph. Second, for each review, we estimate the polarity relative to the features by leveraging the syntactic dependencies between the terms. The approach is evaluated against a ground truth consisting of set of user-generated reviews, manually annotated by 39 human subjects and available online, showing its human-like ability to capture feature-level opinions.