摘要： 随着新技术及社会网络的发展与普及，微博用户数据量剧增，与此相关的研究引起了学术界和工业界的关注。针对中文微博语句特点，通过对比多种特征选取方法，提出一种新的特征统计方法。根据构建的词语字典与词性字典，分析支持向量机、朴素贝叶斯、K最近邻等分类模型，并利用证据理论结合多分类器对中文微博观点句进行识别。采用中国计算机学会自然语言处理与中文计算会议(NLP&CC 2012)提供的数据，运用该方法得到的准确率、召回率和F值分别为70.6%、89.2%、78.9%，而NLP&CC 2012公布的评测结果相应平均值分别为72.7%、61.5%、64.7%，该方法在召回率和F值2个指标上超过其平均值，而F值比NLP&CC 2012评测结果的最好值高出0.5%。

关键词: 微博, 观点句, 支持向量机, 朴素贝叶斯, K近邻, 证据理论

Abstract: With the development and popularity of the new technology and social network, the data volume of micro-blog users surge sharply. Related research causes increasing attention from both academia and industry. This paper proposes a new statistical method on feature extraction. Classification performances of different schemas such as Support Vector Machine(SVM), Naive Bayes and K-Nearest Neighbour(KNN) are analyzed carefully. It proposes a combined model based on D-S theory to take the advantages of different classifiers. A series of experiments based on the Chinese Micro-Blog data provided by CCF NLP&CC 2012 are conducted, and it gets the average estimate 72.7% in precision, 61.5% in recall and 64.7% in F-measure of NLP&CC 2012 as a baseline. Experimental results show that the method can achieve significant enhancement in both recall and F-measure with 70.6%, 89.2% and 78.9%, respectively, and F-measure is even 0.5% higher than the best result of NLP&CC 2012.

Key words: micro-blog, opinion sentence, Support Vector Machine(SVM), Naive Bayes, K-Nearest Neighbour(KNN), D-S theory

中图分类号: 

• TP18