一种级联式微博情感分类器的构建方法

摘要

该文从统计学方法与机器学习的分类思想出发,对中文微博文本的情感类别进行研究.针对微博文本的特点,提出了一种级联式微博情感分类器模型,该模型首先构建基于情感词典和新浪表情符号词典的微博情感初级分类模型;然后根据基准词构建基于类别倾向相似度的二级分类模型,对初级模型未能确定情感类别的微博进行再次分类,并对初级模型的词典进行更新;最后采用朴素贝叶斯分类器构建三级分类模型,对以上还未确定情感类别的微博进行三级分类.通过与NLPCC2014微博情感分类评测参赛结果进行比较,说明了所提方法的有效性.%This paper proposes a cascaded classifier micro-blog sentiment analysis.The primary classifier is based on emotional dictionary and sina micro-blog emoticons dictionary.The secondary classifier is based on the orientation similarity,grouped by several key sentimental word.And the third-level classifier is built by using Na?ve Bayes. The micro-blogs are processed by the three classifiers in a pipeline style.The experimental results show that the method is effective compared against the NLPCC2014 micro-blog sentiment evaluation results.