强语义模糊性词语的情感分析  

Sentiment Analysis with Words of Strong Semantic Fuzziness

作　　者：张志飞[1,3] 苗夺谦[1] 岳晓冬[2] 聂建云[3] 

机构地区：[1]同济大学计算机科学与技术系,上海201804 [2]上海大学计算机工程与科学学院,上海200444 [3]加拿大蒙特利尔大学计算机科学系,蒙特利尔

出　　处：《中文信息学报》2015年第2期68-78,共11页Journal of Chinese Information Processing

基　　金：国家自然科学基金(61273304,61103067);高等学校博士学科点专项科研基金(20130072130004)

摘　　要：语义的模糊性给词语的情感分析带来了挑战。有些情感词语不仅使用频率高,而且语义模糊性强。如何消除语义模糊性成为词语情感分析中亟待解决的问题。该文提出了一种规则和统计相结合的框架来分析具有强语义模糊性词语的情感倾向。该框架根据词语的相邻信息获取有效的特征,利用粗糙集的属性约简方法生成决策规则,对于规则无法识别的情况,再利用贝叶斯分类器消除语义模糊性。该文以强语义模糊性词语"好"为例,对提出的框架在多个语料上进行实验,结果表明该框架可以有效消除"好"的语义模糊性以改进情感分析的效果。Some frequent sentiment words have strong semantic fuzziness, i. e. , have ambiguous sentiment polarities. These words are particularly problematic in word-based sentiment analysis. In this paper, we design an approach to deal with this problem by combining rough set theory and Bayesian classification. To determine the sentiment polarity of a fuzzy word, we use a set of features extracted from its context of utilization. Decision rules based on the features are derived using rough sets. In case the rules fail to classify a case, a Bayes classifier is used as complement. We investigate the case of ＂HAO＂ in Chinese-a very frequent sentiment word, but with many different meanings. The experimental results on several datasets show that our combined method can effectively cope with the semantic fuzziness of the word and improve the quality of sentiment analysis.

关 键 词：情感分析 语义模糊性 粗糙集 贝叶斯分类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]