基于最大熵的中文词语情感分析研究

Sentiment analysis of Chinese words based on maximum entropy

情感词在文本情感分析中处于举足轻重的地位,词语情感倾向的不确定性会受到词语上下文环境的影响。针对词语上下文环境,提出一种基于最大熵模型的词语情感倾向分析方法,从词语上下文中提取词语特征、词语关系特征、词语语义特征和词语情感特征,采用最大熵模型来识别词语的情感倾向,并利用平滑技术解决特征稀疏问题。同时,利用词语与句子之间的情感联系,进一步消除词语情感倾向的不确定性。实验结果表明,该方法在词语情感倾向识别上取得了令人满意的效果。

Emotion words with sentiment polarity play important roles in text sentiment analysis.Uncertainties of sentiment polarity of words are affected by their contexts.In light of these contexts,a method is put forward in this paper to analyze sentiment polarity of words based on maximum entropy models.Features of words,relationships of words,semantic features and emotional characteristics of words are extracted from contexts.Then,sentiment polarity of words is identified by maximum entropy models,and problems concerning sparse features are solved by smoothing techniques.In the meantime,uncertainties of sentiment polarity of words are further eliminated by emotional connections between words and sentences.Experimental results show that this method achieves satisfactory effects in recognizing sentiment polarity of words.