Application of JSA, a Sentiment Analysis Model in Chinese Language and Literature, to Sentiment Recognition in Classical Poetry

Classical poetry embodies the essence of traditional Chinese culture, and its lyricism and infectiousness provide an ideal platform for educating about emotions. However, due to the West’s influence in modern times, the teaching of classical poetry has not expanded to include the emotional aspect. Therefore, this paper establishes the JSA model as the research model for recognizing emotions in classical poetry, based on an analysis of existing methods for recognizing emotions in Chinese literature. Upon scrutinizing the JSA model’s construction, we discovered that it overly relies on the distribution of emotions for theme generation. Consequently, this paper enhances the JSA model by situating the emotion layer between the theme layer and the word layer, builds the reverse JSA model, and employs Bayesian estimation to estimate the model’s parameters. In this paper, we use classical poems as an example to demonstrate how to analyze the sentiment of classical poems by recognizing tone auxiliaries. The improved JSA model’s emotion recognition effect closely aligns with the actual expression effect of the poems, demonstrating the effective application of the advanced JSA model in this paper for emotion recognition of classical poems.