Sentiment analysis of Japanese text and vocabulary learning based on natural language processing and SVM

Abstract

The traditional Japanese text sentiment classification mainly relies on some domain experts to classify text sentiment according to their knowledge, which is time-consuming and laborious. In order to improve the effect of text sentiment classification, this study combines intelligent machine algorithms to study Japanese text sentiment classification, which provides a theoretical reference for subsequent natural language research. Moreover, this study combines the TF-IDF algorithm with SVM to construct a Japanese text sentiment classification model and proposes a chi-square statistic that combines word frequency factor, inter-class concentration coefficient, and correction coefficient. Aiming at the problem of traditional feature weighting method TFIDF ignoring the distribution of feature items inter-class and intra-class when calculating the weight of feature items, chi-square statistics is introduced to improve the effect that TFIDF ignores the distribution of feature items in inter-class, and intra-class information entropy is introduced to improve the effect that TFIDF ignores the distribution of feature items in the intra-class. In addition, this study designs a control experiment to analyze the performance of the model proposed in this study. Through various comparative analysis, it shows that the research model has good comprehensive performance, meets the needs of sentiment classification system, and can provide theoretical reference for related research.