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Research on Discrete Emotion Classification of Chinese Online Product Reviews Based on OCC Model

机译:基于OCC模型的中文在线产品评论离散情感分类研究

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There are many rich and multi-dimensional discrete emotional information in online reviews. The research of discrete emotion analysis in English online reviews is relatively early and has made some achievements, but the related research on Chinese online reviews, especially in consumption field is still in its infancy, which is the focus of this study. By taking the product reviews of a large Chinese e-commerce platform as the research object, and using the OCC model which is a kind of emotional cognition model as the basis of emotion classification, the study constructs six datasets annotated with six discrete emotions including satisfaction, disappointment, admiration, reproach, like and hate. CHI-based feature selection is performed for each review sample of the above six datasets and five traditional classifiers are used to classify the discrete emotions. The experimental result demonstrates the effectiveness of our proposed approach and shows that the classifier based on LR algorithm has the best classification effect for each discrete emotion.
机译:在线评论中有许多丰富且多维的离散情感信息。英文在线评论中的离散情感分析研究相对较早并取得了一些成就,但是有关中文在线评论的相关研究,尤其是在消费领域,仍处于起步阶段,这是本研究的重点。通过以大型中国电子商务平台的产品评论为研究对象,并使用一种作为情感认知模型的OCC模型作为情感分类的基础,该研究构建了六个数据集,这些数据集包含六个离散的情感,包括满意度,失望,钦佩,责备,喜欢和恨。对以上六个数据集的每个评论样本执行基于CHI的特征选择,并使用五个传统分类器对离散情绪进行分类。实验结果证明了该方法的有效性,并表明基于LR算法的分类器对每种离散情感具有最佳的分类效果。

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