Student emotion classification based on online comment text big data mining Online publication date: Fri, 25-Oct-2024
by Zhiling Yang
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 16, No. 4, 2024
Abstract: In order to solve the problems of low recall and accuracy in traditional student emotion classification methods and long time-consuming student emotion classification, a student emotion classification method based on online comment text big data mining was proposed. Firstly, Selenium is used to crawl the online review text big data, and mean shift clustering algorithm is used to cluster and mine the crawled data. Secondly, logical regression model and sorting neighbour method are used to complete missing data filling and duplicate data cleaning. Finally, the topic model is used to extract the subject words from the online review text data, and calculate the characteristics of the online review text data. Combined with XGBoost, students' emotions are classified. The experimental results show that the maximum classification recall rate of this method is 97%, the maximum accuracy rate is 98%, and the maximum classification time is 0.84 s.
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