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Sentiment Classification for Chinese Reviews Using Machine Learning Methods Based on String Kernel

机译:使用基于String Kernel的机器学习方法进行情感分类

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Sentiment classification aims at mining reviews of people for a certain event's topic or 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 decision maker and ordinary people. 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 reviews classification, few jobs have been done on sentiment classification for Chinese reviews. The machine learning approach Based on string kernel for sentiment classification on reviews written in Chinese was proposed in this paper. Data experiment shows the capability of this approach.
机译:情感分类目标是通过自动将审查分类为积极或负面意见,为某个事件的主题或产品进行挖掘审查。随着万维网应用的快速发展,情绪分类将有很大的机会,帮助人们从网络信息自动分析客户的意见。自动意见采矿将使决策者和普通人受益。到目前为止,它仍然是一个复杂的任务,挑战很大。主要有两种类型的情绪分类方法,机器学习方法和语义定向方法。虽然一些先锋研究探索了英语评论分类的方法,但仍有很少有工作的中国评论。本文提出了基于STRING KENEL的机器学习方法,以汉语编写的评论中的评论分类。数据实验显示了这种方法的能力。

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