PMI与Hownet结合的中文微博情感分析

Chinese Microblog Polarity Classification Based on Hownet and PMI

为解决中文微博情感的分类问题,文中提出了基于微博数据将PMI与Hownet相结合的情感分类方法。通过对微博数据短小、新颖特征的研究,提出词典合并方法。将现有词典按照Hownet词语相似度合并,利用PMI对网络词语进行情感分类。添加网络情感词构造适应微博文本特征的情感词典,并在新词典的基础上结合监督学习方法训练情感分类模型。实验结果表明,用此方法进行情感分析能够有效识别网络新词对情感分析的影响,准确率可达78.3%,在对含有网络新词的微博情感分析上,该方法相比仅使用词典或者监督学习的准确率更高。

To solve the problem of Chinese microblog sentiment classification,a sentiment classification method combining PMI and Hownet based on microblog data is proposed.Through the research on the short and novel features of microblog data,a method of dictionary merging is proposed.The existing dictionaries are merged according to Hownet word similarity,and PMI is used to perform sentiment classification of online words.The network sentiment words are added to construct sentiment dictionary that adapts to the features of microblog text,and sentiment classification models are trained based on the new dictionary combined with supervised learning methods.The experimental results show that using this method for sentiment analysis can effectively identify the impact of new internet words on sentiment analysis,with an accuracy rate of 78.3%.In the sentiment analysis of microblog containing new words on the Internet,the accuracy rate is higher than that of only using dictionaries or supervised learning.