摘要
博客作为一种大众化的信息及文化载体被越来越多的人所接受,博客信息的情感分析也逐渐成为了信息挖掘领域的热点。目前,在研究情感分析时,多是通过计算词汇的倾向性来完成的。由于并不是所有的带有情感色彩的词汇都是主题相关的,因此,以词为粒度的情感分析存在一定的缺陷。为了解决这一问题,试图从句子层面进行分析,主要研究了与之相关的主题情感句的自动提取问题。为了有效地提取主题相关情感句,设计了一个新颖的基于二元切分的提取算法来获取主题词,然后利用TFIDF算法获取更多的次要主题词,并利用这些主题词重组了那些包含主题词的原始句。因此,如果主题情感句存在的话,那么它一定在这些重组的主题句集合中,只要对该重组句集合进行分析、提取,便能得到主题情感句。最后,利用CRFs将主题句提取问题有效转化为了中文chunking问题,并在抽取实验中取得了很好的结果。
In the field of Chinese blog sentiment analysis,previous researchers put most energy on the polarity analysis of word, but not all the word analyzed is relative with the topic,and word-level granularity for sentiment analysis is too small.We try to use sentiment sentences,a sentence-level model,for sentiment analysis.In this paper,it only focuses on topic sentiment sentences auto-extraction.In order to extracting topic sentiment sentences,it designs a novel Bi-segment method to extract the main topic words,and uses TFIDF to extract more topic words.With these words,it recombines original sentences,which contain the topic words.So as long as topic sentiment sentences exist,they must in the set of recombined sentences.Then,based on the analysis of Chinese blogs,it converts the problem of extraction into Chinese chunking by CRFs and has a good performance in extraction experiment.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第20期165-168,221,共5页
Computer Engineering and Applications
关键词
中文博客
情感分析
CRFS
Chinese blogs
sentiment analysis
Conditional Random Fields（CRFs ）
