基于集成分类器的产品评论意见挖掘

G. Vinodhini, R. Chandrasekaran
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引用次数: 5

摘要

在线产品评论被认为是一种重要的信息资源,对消费者和制造商都很有用。在线评论是自然语言形式的无结构文本。手动浏览大量的审查是非常繁琐和耗时的任务。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(即积极或消极意见)对评论进行分类的任务。本文主要研究了支持向量机集成方法在意见挖掘中的应用。对三种不同产品的基于特征的产品评论数据集进行了集成分类器测试。结果表明,基于支持向量机集成的意见挖掘方法在错误率和接收者工作特征曲线方面优于单个基线方法。关键词:意见,分类,机器学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Product Review Mining for Opinion Using Ensemble Classifier
Online product reviews is considered as a major informative 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 volume 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 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. Key words: Opinion, Classification, Machine Learning.
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