Sentiment Analysis of International and Foreign Chinese-Language Texts with Multilevel Features

Abstract

This paper takes the application of international Chinese to foreigners on the Internet as the research object. A variety of features are constructed according to the characteristics of international and foreign Chinese texts and networks. This paper selects three features: dictionary-based sentiment value feature, expression feature, and improved semantic feature. A text sentiment classification model is formed by fusing multiple features. Compared with the traditional model and other single-feature models on the self-built dataset, the experimental results show that its sentiment classification ability has been effectively improved. The results show that the accuracy, recall, and F1 value of the fused multilevel feature MFCNN model are much higher than the accuracy, recall, and F1 value of other models. This also shows that the improved model of this method has a better effect of improving the accuracy.