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Received:November 15, 2010 Revised:December 24, 2010
Received:November 15, 2010 Revised:December 24, 2010
中文摘要: 通过基于概念的聚类方法,对博客作者的情感极性进行分析.在知网情感词汇库的基础上,将概念引入向量空间模型.首先,提取博客文本情感词,利用基于情感词概念的向量空间模型完成对博客文本的表示.然后,使用k-means算法对博客文本进行聚类,完成对博客情感极性的分析.在向量空间模型中使用概念作为特征项,提高了对博客作者情感极性分析的精度.实验证明基于概念的向量空间模型比传统基于词语的向量空间模型在博客文本情感聚类上具有更好的性能.
Abstract:A clustering method based on concept was provided to analyse the sentiment polarity for Chinese Bloggers. The concept is introduced into Vector Space Model (VSM) on the basis of HowNet. Firstly, sentiment words are extracted from blog texts which would be expressed by VSM with the concept of sentiment words. Secondly, blog texts are clustered with k-means algorithm to finish the analysis of sentiment polarity for Chinese Blogs. The precision of sentiment polarity analysis of Chinese Blogs is improved with concept as feature in VSM. The experiment proves the concept based VSM to be of better performance than traditional term based VSM in clustering analysis of Chinese Blogs on sentiment polarity.
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引用文本：
申莹,徐东平,庞俊.基于概念的中文博客情感极性聚类分析.计算机系统应用,2011,20(8):72-75,121
SHEN Ying,XU Dong-Ping,PANG Jun.Clustering Analysis of Sentiment Polarity for Chinese Blogs Based on Concept.COMPUTER SYSTEMS APPLICATIONS,2011,20(8):72-75,121
申莹,徐东平,庞俊.基于概念的中文博客情感极性聚类分析.计算机系统应用,2011,20(8):72-75,121
SHEN Ying,XU Dong-Ping,PANG Jun.Clustering Analysis of Sentiment Polarity for Chinese Blogs Based on Concept.COMPUTER SYSTEMS APPLICATIONS,2011,20(8):72-75,121
