Mining Thematic Trends in Chinese Literature Using Text Mining Technology

Abstract

Text mining technology combined with mining themes has become a powerful tool for studying works of Chinese literature. This study introduces a new way to measure the features of text mining by combining mining algorithms. This makes it less subjective to determine the value and language of ancient Chinese literature. This paper shows that the model can correctly classify text mining in Chinese literature words with a maximum level of accuracy. The study propose a Text mining based Enhanced hierarchical based gradient boost algorithm (TEHGBA) model for analyzing the thematic trends in the Chinese literature. This new TEHGBA model gets better scores for all the different Chinese Literature results. The study results found that the proposed model has provided an accuracy of 98.6%. The study conclude that the proposed model helps in analyzing Chinese literary works and get good results in terms of recall, accuracy, and precision.