Merging Naive Bayes and Causal Rules for Text Sentiment Analysis. Issue 1 (January 2021) - Luo, Yongjian; Yang, Xiaohua; Ouyang, Chunping; Wan, Yaping; He, Sixi
Abstract: Trad itional machine learning sentiment analysis models are d ifficult to achieve good classification results from small sample data. This paper proposes to merging naive Bayes and causal rule(MNBACR) for small sample data sentiment analysis scenarios. This model is based on the causal analysis theory, and introduce the causal inference algorithm into the field of text sentiment analysis. The causal inference algorithm extracts the causal ru les of Chinese texts, and the causal rules can be used as the features of the naive Bayes algorithm to predict the sentiment polarity of small sample texts. In experiments, the model in this paper is evaluated on financial news datasets which have a small number for sample, and the results show that the proposed method achieves the best performance compared to the existing state-of-the-art models on the small sample data onto sentiment analysis