摘要
基于朴素贝叶斯理论提出了一种新的中文文本情感分类方法。这种方法利用情感词典对文本进行处理和表示,基于朴素贝叶斯理论构建文本情感分类器,并以互联网上宾馆中文评论作为分类研究的对象。实验表明,使用提出的方法构成的分类器具有分类速度快、分类准确度高、鲁棒性强等特点,并且适合于大量中文文本情感分类应用系统。
This paper provided a new classification approach of Chinese texts based on naive Bayesian. The approach reached its goal by applying semantic lexicon on text processing and expressing,constructing sentiment classifier based on naive Bayesian and experimental data obtained from hotel’s Chinese reviews through Internet service. Backed with the experimental data, this approach demonstrates its efficiency,accuracy and robustness,which makes it applicable as well in sentiment classification for plenty of Chinese texts.
出处
《计算机应用研究》
CSCD
北大核心
2010年第10期3737-3739,3743,共4页
Application Research of Computers
基金
湖南省教育厅科学研究资助项目(07B014)
广东省自然科学基金资助项目(9151805707000010)
广州市社科规划项目(08Y59)
关键词
文本情感分类
朴素贝叶斯
情感词典
text sentiment classification
naive Bayesian
semantic lexicon
