Abstract:

Chinese sentiment analysis is one of important researches in natural language processing, which aims to discover the sentimental tendencies in the Chinese text. In recent years, research on Chinese text sentiment analysis has made great progress in efficiencies, but few studies have explored the characteristics of the language and downstream task requirements. Therefore, in view of the particularity of Chinese text and the requirements of sentiment analysis, using the Chinese text sentiment analysis method that integrates multi-granularity semantic features, such as characters, words, radicals, and part-of-speech is proposed. This introduces radical features and emotional part-of-speech features based on character and word features. Additionally, this integration uses bidirectional the long short-term memory network (BLSTM), attention mechanism and recurrent convolutional neural network (RCNN). The softmax function is used to predict the sentimental tendencies by integrating multi-granularity semantic features. The comparative experiment results on the NLPECC (natural language processing and Chinese computing) dataset showed that the F₁ score of the proposed method was 84.80%, which improved the performance of the existing methods to some extent and completed the Chinese text sentiment analysis task.

Key words: Chinese text, multi-granularity semantic features, sentiment analysis, big data environment