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A Text Mining Approach to Online Reviews for Opinion Mining Using Ensemble

机译:使用集成的文本挖掘在线评论意见挖掘方法

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Online product reviews are considered as a major information resource which is useful for both customers and manufacturers. The online reviews are unstructured-free-texts in natural language form. The task of manually scanning through huge volumes of review is very tedious and time consuming. Therefore it is needed to automatically process the online reviews and provide the necessary information in a suitable form. In this paper, we dedicate our work to the task of classifying the reviews based on the opinion, i.e. positive or negative opinion. This paper mainly addresses using the ensemble approach of Support Vector Machine (SVM) for opinion mining. Ensemble classifier was examined for feature based product review dataset for three different products. We showed that proposed ensemble of Support Vector Machine is superior to individual baseline approach for opinion mining in terms of error rate and Receiver operating characteristics Curve.
机译:在线产品评论被视为主要的信息资源,对客户和制造商都有用。在线评论是自然语言形式的非结构化自由文本。手动扫描大量审阅的任务非常繁琐且耗时。因此,需要自动处理在线评论并以合适的形式提供必要的信息。在本文中,我们致力于基于意见(即正面或负面意见)对评论进行分类的任务。本文主要针对使用支持向量机(SVM)的集成方法进行观点挖掘的问题。对Ensemble分类器检查了三个不同产品的基于功能的产品评论数据集。我们表明,在错误率和接收器工作特性曲线方面,所提出的支持向量机集成在观点挖掘方面优于单个基线方法。

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