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Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:p>Student Teaching Feedback Sentiment Analysis (STFSA) plays a crucial role in evaluating teaching effectiveness, and its analysis results are influenced by both teaching process organization and students\u2019 course cognition. However, current sentiment analysis of students\u2019 feedback faces the challenges of integrating fine-grained entity-level sentiment and multi-polar sentiment in overall sentiment analysis. Therefore, we propose a novel sentiment analysis strategy for Entity-level and Entirety-level Teaching evaluation Sentiment analysis that considers students\u2019 Cognitive abilities (EETSC). This strategy utilizes two-level networks: one for the entity level network based on dual attention mechanism to conduct fine-grained sentiment analysis of students\u2019 feedback, and the other for the overall sentiment analysis network that integrates students\u2019 personalized cognitive abilities to adjust the sentiment characteristics of different entities and obtain more accurate and reasonable emotions at the overall level. Experimental results show that, compared to the state-of-the-art baseline, EETSC achieves an accuracy of 86.34% and an F1 score of 77.13% in the recognition of six types of teaching entity sentiments, representing improvements of 2.19% and 2.18%, respectively. For entirety-level sentiment recognition, EETSC achieves an accuracy of 87.36% and an F1 score of 78.02%, with improvements of 0.76% and 1.21%. Further experimental analysis indicates that EETSC can alleviate the problem of sentiment polarity conflicts in teaching evaluations and provides a solution for integrating students\u2019 cognitive states into teaching sentiment analysis in the field of educational natural language processing.<\/jats:p>","DOI":"10.1145\/3766517","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T11:41:11Z","timestamp":1757590871000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Considering Student's Cognitive Abilities-Entity Level and Entirety Level Sentiment Analysis of Teaching Evaluation"],"prefix":"10.1145","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7102-9028","authenticated-orcid":false,"given":"Ting","family":"Cai","sequence":"first","affiliation":[{"name":"School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications","place":["Chongqing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6827-146X","authenticated-orcid":false,"given":"Yu","family":"Xiong","sequence":"additional","affiliation":[{"name":"Center of Artificial Intelligence and Intelligent Education Research, Chongqing University of Posts and Telecommunications","place":["Chongqing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4730-5552","authenticated-orcid":false,"given":"Xinming","family":"Qin","sequence":"additional","affiliation":[{"name":"Center of Artificial Intelligence and Intelligent Education Research, Chongqing University of Posts and Telecommunications","place":["Chongqing, China"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2411-3742","authenticated-orcid":false,"given":"Yu","family":"Yao","sequence":"additional","affiliation":[{"name":"Center of Artificial Intelligence and Intelligent Education Research, Chongqing University of Posts and Telecommunications","place":["Chongqing, China"]}]}],"member":"320","published-online":{"date-parts":[[2025,10,24]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","DOI":"10.9781\/ijimai.2023.02.003","article-title":"Emotion-aware monitoring of users\u2019 reaction with a multi-perspective analysis of long- and short-term topics on Twitter","author":"Cavaliere Danilo","year":"2023","unstructured":"Danilo Cavaliere, Giuseppe Fenza, Vincenzo Loia, and Francesco David Nota. 2023. 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