基于改进语义导向的中文电影评论情感分类

Q. Ye, Wen Shi, Yijun Li
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引用次数: 79

摘要

情感分类的目的是挖掘顾客对某种产品的评论,将评论自动分类为正面或负面意见。随着万维网应用的快速发展,情感分类将有很大的机会帮助人们从网络信息中自动分析客户的意见。自动意见挖掘对消费者和卖家都有利。到目前为止,这仍然是一项复杂的任务,具有很大的挑战性。情感分类主要有两种方法,机器学习方法和语义导向方法。虽然有一些先驱研究探索了英语影评分类的方法,但对中文影评情感分类的研究却很少。提出了一种改进的中文影评情感分类语义方法。数据实验证明了该方法的有效性。
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Sentiment Classification for Movie Reviews in Chinese by Improved Semantic Oriented Approach
Sentiment classification aims at mining reviews of customers for a certain product by automatic classifying the reviews into positive or negative opinions. With the fast developing of World Wide Web applications, sentiment classification would have huge opportunity to help people automatic analysis of customers’ opinions from the web information. Automatic opinion mining will benefit to both consumers and sellers. Up to now, it is still a complicated task with great challenge. There are mainly two types of approaches for sentiment classification, machine learning methods and semantic orientation methods. Though some pioneer researches explored the approaches for English movie review classification, few jobs have been done on sentiment classification for Chinese reviews. The improved semantic approach for sentiment classification on movie reviews written in Chinese was proposed in this paper. Data experiment shows the capability of this approach.
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