关键词: 极性词典, 语素模型, 同义关系, 图模型, 标签传播

Abstract: Concerning the dependence on seed words amount of the traditional method based on morpheme, and the low recall rate of traditional graph-based method, the authors proposed a method which integrated the morpheme relationship of Chinese words into the graph model, and combined the synonymy of words to build Chinese polarity lexicon by a semi-supervised learning algorithm in a graph. Firstly, a morpheme model was used to weight the similarity of two Chinese words. Secondly, synonymous words and bilingual lexicon were used to build the synonymy of words. Finally, the final relation was acquired by integrating the two relations, and Label Propagation (LP) was used to run on the relation map to distinguish the polarity of the emotion words. The experimental results show that the proposed method can achieve high accuracy and recall rate, and MicroF1 can be as high as 92.8%. The dependence on seed words amount is reduced based on the fact that when the seed word amount is 100, MicroF1 can still be 84.1%. In addition, the proposed method has fast convergence.

Key words: polarity lexicon, morpheme model, synonymy relation, graph model, Label Propagation (LP)