Optimizing modularity to identify semantic orientation of Chinese words

Inferring the semantic orientation of subjective words (including adjectives, adverbs, nouns, and verbs) is an important task for sentiment analysis of texts. This paper proposes a novel algorithm, which attempts to attack this problem by optimizing the modularity of the word-to-word graph. Experimental results indicate that proposed method has two main advantages: (1) by spectral optimization of modularity, proposed approach displays a higher accuracy than other methods in inferring semantic orientation. For example, it achieves an accuracy of 88.8% on the HowNet-generated test set and (2) by effective usage of the global information, proposed approach is insensitive to the choice of paradigm words. In our experiment, only one pair of paradigm words is needed.