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Title: Intelligent emotion classification for college students in online education based on text clustering modelling

Authors: Yesi Tang

Addresses: School of Economics and Management, Chongqing Industry Polytechnic University, Chongqing, 401120, China

Abstract: To tackle the declining precision of sentiment analysis for online educational website reviews via the LDA topic model, I propose the innovative TWBEWC-TFWW-LDA algorithm, integrating emotion word co-occurrence-based theme word bags (TWBEWC) and topic feature word weighting (TFWW). It constructs emotional topic word bags, extracts sentiment-laden topic words via semantic similarity, weights them by significance and distribution, and performs LDA clustering. Experiments show that with 15 emotion topic feature words, its text clustering accuracy, recall and F1 reach 0.812, 0.802 and 0.810 respectively; it also achieves 88%, 96% and 90% accuracy in classifying aversion, surprise and neutrality. This enhanced accuracy refines online education sentiment analysis for college students, optimising course design and teaching methods.

Keywords: online education; LDA topic model; sentiment classification; sentiment word co-occurrence; feature word weighting.

DOI: 10.1504/IJART.2026.154949

International Journal of Arts and Technology, 2026 Vol.16 No.7, pp.97 - 114

Received: 24 Dec 2025
Accepted: 01 Mar 2026
Published online: 20 Jul 2026 *