基于Good-Turing平滑SO-PMI算法构建微博情感词典方法的研究

Research on the Construction of Micro-blog Sentiment Lexicon Based on Good-Turing Smoothing SO-PMI Algorithm

微博情感词典的构建在微博情感分析中具有重要的研究意义和使用价值。针对现有公共情感词典对微博中情感词覆盖率较低的问题,以HowNet和大连理工大学情感本体作为微博基础情感词典,提出一种基于Good-Turing平滑的SO-PMI算法,针对近年来出现的大量网络情感词汇进行情感倾向性的判断,并与基础情感词典融合构建微博领域情感词典。最后采用基于规则的方法判断微博文本的情感倾向性。实验结果验证上述方法构建的微博领域情感词典在微博情感分类中的有效性和准确性。

Construction of micro-blog sentiment lexicon has important research significance and use value. In view of the problem that the existing sentiment lexicon has a low coverage rate of the sentiment words in micro-blog, puts forward an SO-PMI algorithm based on Good-Turing smoothing to extend micro-blog sentiment lexicon on the basis of How Net and Dalian University of Technology Emotional Ontology, then uses rule-based method to judge the emotional tendency of experimental data. Experimental results showed that the method has good effectiveness and accuracy of sentiment classification.