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Mining hidden opinions from objective sentences

机译：从客观句子中挖掘隐藏的观点

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摘要

Sentiment analysis and opinion mining is a very popular and active research area in natural language processing, it deals with structured and unstructured data to identify and extract people's opinions, sentiments and emotions in many resources of subjectivity such as product reviews, blogs, social networks, etc. All existing feature-level opinion mining approaches deal with the detection of subjective sentences and eliminate objective ones before extracting explicit features and their related positive or negative polarities. However, objective sentences can carry implicit opinions and a lack attention given to such sentences can adversely affect the obtained results. In this paper, we propose a classification-based approach to extract implicit opinions from objective sentences. Firstly, we apply a rule-based approach to extract explicit feature-opinion pairs from subjective sentences. Secondly, in order to build a classification model, we construct a training corpus based on extracted explicit feature-opinion pairs and subjective sentences. Lastly, mining implicit feature-opinion pairs from objective sentences is formulated into a text classification problem using the model previously built. Tested on customer reviews in three different domains, experimental results show the effectiveness of mining opinions from objective sentences.
机译：情感分析和观点挖掘在自然语言处理中是一个非常受欢迎且活跃的研究领域，它处理结构化和非结构化数据，以识别和提取人们在许多主观性资源中的观点，情感和情感，例如产品评论，博客，社交网络，现有的所有特征级意见挖掘方法都在提取显式特征及其相关的正负极性之前处理主观句子的检测并消除客观句子。但是，客观句子可能带有隐含的观点，而对此类句子的关注不足会对获得的结果产生不利影响。在本文中，我们提出了一种基于分类的方法来从目标句子中提取隐式意见。首先，我们应用基于规则的方法从主观句子中提取显式特征-观点对。其次，为了建立分类模型，我们基于提取的显式特征-观点对和主观句子构造训练语料库。最后，使用先前构建的模型将从目标句子中挖掘隐式特征-观点对制定为文本分类问题。在三个不同领域对客户评论进行测试，实验结果表明从客观句子中挖掘观点的有效性。

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