基于知识语义权重特征的朴素贝叶斯情感分类算法

针对文档级情感分类的准确率低于普通文本分类的问题,提出一种基于知识语义权重特征的朴素贝叶斯情感分类算法.首先,通过特征选择的方法,对情感词典中的词进行重要度评分并赋予不同权重.然后,基于词典极性的分布信息与文档情感分类的相关性,将情感词的语义权重特征融合到朴素贝叶斯分类中,实现了新算法.在标准中文数据集上的实验结果表明,提出的算法在准确率、召回率和F1测度值上都优于已有的一些算法.

Semantic Weight-based Naive Bayesian Algorithm for Text Sentiment Classification

To solve the drawback that the precision of the document-level sentiment classification is lower than that of the normal text classification,this paper proposes a semantic weight-based Native Bayesian algorithm for text sentiment classification. First,the words in an emotion dictionary were scored and weighted using a feature selection method. Second,based on the correlation between the distribution of dictionary polar and the document-level sentiment classification,the semantic weight feature was merged into naive Bayesian classification and a new algorithm was achieved. Finally,lots of experiments on some standard Chinese data sets were performed. Results show that this algorithm is better than some existing algorithms on precision,recall,and F1-measure.