关键词: 情感词典, 语用知识, 非负矩阵分解, 共现关系, 标签传播

Abstract: Aimed at the problems that the Chinese polarity lexicons only reflect the language knowledge, lack pragmatic knowledge, this paper proposes a method which obtains the co-occurrence relations between words from real corpora, and combines the synonymy and morpheme features of words to build Chinese polarity lexicon by a semi-supervised learning algorithm. Firstly, a relation matrix is constructed between the emotion word and the evaluation object from corpora by PMI（Point-wise Mutual Information）. Secondly, it uses NMF（Non-negative Matrix Factorization） down to its co-occurrence matrix between emotion words, and new matrix of emotion words and evaluation object. Finally, the two relation matrix is combined with the feature of synonymy and morpheme, and Label Propagation（LP） algorithm is used to run the relation map to distinguish the polarity of the emotion words. Experimental results show that the proposed method improves the accuracy and recall compared with only considering morpheme and semantic method on the same data set.

Key words: polarity lexicons, pragmatic knowledge, non-negative matrix factorization, co-occurrence, Label Propagation（LP）