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Semantic Approaches to Fine and Coarse-Grained Feature-Based Opinion Mining

机译：基于细粒度特征的意见挖掘的语义方法

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摘要

Feature-based opinion mining from product reviews is a difficult task, both due to the high semantic variability of opinion expression, as well as because of the diversity of characteristics and sub-characteristics describing the products and the multitude of opinion words used to depict them. Further on, this task supposes not only the discovery of directly expressed opinions, but also the extraction of phrases that indirectly or implicitly value objects and their characteristics, by means of emotions or attitudes. Last, but not least, evaluation of results is difficult, because there is no standard corpus available that is annotated at such a fine-grained level and no annotation scheme defined for this purpose. This article presents our contributions to this task, given by the definition and application of an opinion annotation scheme, the testing of different methodologies to detect phrases related to different characteristics and the employment of Textual Entailment recognition for opinion mining. Finally, we test our approaches both on the built corpus, as well as on an ad-hoc built collection of reviews that we evaluate on the basis of the stars given. We prove that our approaches are appropriate and give high precision results.
机译：从产品评论中进行基于特征的观点挖掘是一项艰巨的任务，这既由于观点表达的语义可变性高，又由于描述产品的特征和子特征的多样性以及用于描述产品的大量观点词。此外，该任务不仅需要发现直接表达的观点，而且还需要提取通过情感或态度间接或隐含地评价对象及其特征的短语。最后但并非最不重要的一点是，结果评估很困难，因为没有可用的标准语料库在这种细粒度的级别上进行注释，也没有为此目的定义的注释方案。本文介绍了我们对这一任务的贡献，包括意见注释方案的定义和应用，测试不同方法以检测与不同特征相关的短语以及使用文本蕴涵识别进行意见挖掘。最后，我们在构建的语料库以及在基于给定星号进行评估的临时构建的评论集合上测试我们的方法。我们证明了我们的方法是适当的，并且给出了高精度结果。

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