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。%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.
展开▼
