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Abstrakty
The widespread use of social networks has provided a variety of active, dynamic, and popular platforms for students to express their opinions and sentiments.These data are increasingly being exploited and integrated into university information systems to better govern and manage universities and improve educational quality. The analysis of such data can offer valuable insights into student experiences and attitudes towards various educational aspects including courses, professors, events, and facilities. However, automatic opinion mining in this context is challenging due to the difficulty of analyzing some languages suchas Arabic, the variety of used languages, the presence of informal language, theuse of emoticons and emoji, sarcasm, and the need to consider the surrounding context. To deal with all these challenges, we propose a novel approach for an effective sentiment analysis of student comments on the X platform (Twitter).The proposed approach allows the collection of student comments from public Twitter pages and automatically classifies comments into positive, negative, and neutral. The new approach is based on ChatGPT capabilities, supports three languages: English, Arabic, and colloquial Arabic, and integrates a news coring method that measures both the positiveness and subjectivity of student comments. Experiments performed on simulated and real public Twitter pages of five Saudi high education institutions showed the performance of the proposed tool to analyze and summarize collected data automatically.
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Czasopismo
Rocznik
Tom
Strony
101--131
Opis fizyczny
Bibliogr. 33 poz., rys., tab., wykr.
Twórcy
Bibliografia
- [1] Alencia Hariyani C., Nizar Hidayanto A., Fitriah N., Abidin Z., Wati T.:Mining Student Feedback to Improve the Quality of Higher Education through Multi Label Classification, Sentiment Analysis, and Trend Topic. In: 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pp. 359–364, 2019. doi: 10.1109/ICITISEE48480.2019.9003818.
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- [11] Hew K.F., Hu X., Qiao C., Tang Y.: What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach, Computers&Education, vol. 145, 103724, 2020. doi: 10.1016/j.compedu.2019.103724.
- [12] Jasim Y., Saeed M., Raewf M.B.: Analyzing Social Media Sentiment: Twitteras a Case Study,ADCAIJ: Advances in Distributed Computing and ArtificialIntelligence Journal, vol. 11, pp. 427–450, 2023. doi: 10.14201/adcaij.28394.
- [13] Kastrati Z., Dalipi F., Imran A.S., Pireva Nuci K., Wani M.A.: Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study, Applied Sciences, vol. 11(9), 2021. doi: 10.3390/app11093986.
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- [15] Kavitha R.: Sentiment Research on Student Feedback to Improve Experiences in Blended Learning Environments, International Journal of InnovativeTechnology and Exploring Engineering, pp. 159–163, 2019. doi: 10.35940/ijitee.K1034.09811S19.
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- [17] Li X., Zhang H., Ouyang Y., Zhang X., Rong W.: A Shallow BERT-CNN Model for Sentiment Analysis on MOOCs Comments. In: 2019 IEEE International Conference on Engineering, Technology and Education (TALE), 2019.doi: 10.1109/TALE48000.2019.9225993.
- [18] Liu Y., Ott M., Goyal N., Du J., Joshi M., Chen D., Levy O.,et al.: RoBERTa: A Robustly Optimized BERT Pretraining Approach, ArXiv, vol. abs/1907.11692, 2019. doi: 10.48550/arXiv.1907.11692.
- [19] Manzoor U., Baig S.A., Hashim M., Sami A.: Impact of Social Media Marketingon Consumer’s Purchase Intentions: The Mediating Role of Customer Trust, International Journal of Entrepreneurial Research, vol. 3(2), pp. 41–48, 2020.doi: 10.31580/ijer.v3i2.1386.
- [20] Misuraca M., Scepi G., Spano M.: Using Opinion Mining as an educational analytic: An integrated strategy for the analysis of students’ feedback, Studies in Educational Evaluation, vol. 68, 100979, 2021. doi: 10.1016/j.stueduc.2021.100979.
- [21] Nasim Z., Rajput Q., Haider S.: Sentiment analysis of student feedback using machine learning and lexicon based approaches. In: 2017 International Conference on Research and Innovation in Information Systems (ICRIIS), 2017.doi: 10.1109/ICRIIS.2017.8002475.
- [22] Nguyen D.Q., Vu T., Nguyen A.T.: BERTweet: A pretrained language modelfor English Tweets. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 9–14, 2020.doi: 10.18653/v1/2020.emnlp-demos.2.
- [23] Obinwanne T., Brandtner P.: Enhancing Sentiment Analysis with GPT – A Comparison of Large Language Models and Traditional Machine Learning Techniques. In: A.K. Nagar, D.S. Jat, D. Mishra, A. Joshi (eds.),Intelligent Sustainable Systems. Selected papers of WorldS4 2023, Volume 3, Lecture Notes in Networks and Systems, vol. 803, pp. 187–197, Springer Nature, Singapore, 2024. doi: 10.1007/978-981-99-7569-3_17.
- [24] Osmanoğlu U.Ö., Atak O.N., Çağlar K., Kayhan H., Can T.: Sentiment Analysis for Distance Education Course Materials: A Machine Learning Approach, Journal of Educational Technology and Online Learning, vol. 3, pp. 31–48, 2020. doi: 10.31681/jetol.663733.
- [25] Rajput Q., Haider S., Ghani S.: Lexicon-Based Sentiment Analysis of TeachersEvaluation, Applied Computational Intelligence and Soft Computing, vol. 2016(1), 2385429, 2016. doi: https://doi.org/10.1155/2016/2385429.
- [26] Sangeetha K., Prabha D.: RETRACTED ARTICLE: Sentiment analysis of student feedback using multi-head attention fusion model of word and context embedding for LSTM, Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 4117–4126, 2020. doi: 10.1007/s12652-020-01791-9.
- [27] Sindhu I., Muhammad Daudpota S., Badar K., Bakhtyar M., Baber J., Nurunnabi M.: Aspect-Based Opinion Mining on Student’s Feedback for FacultyTeaching Performance Evaluation, IEEE Access, vol. 7, pp. 108729–108741, 2019.doi: 10.1109/ACCESS.2019.2928872.
- [28] Srinivas S., Rajendran S.: Topic-based knowledge mining of online student reviews for strategic planning in universities, Computers and Industrial Engineering, vol. 128, pp. 974–984, 2019. doi: 10.1016/j.cie.2018.06.034.
- [29] Susnjak T.: Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature, vol. 2742, pp. 173–183, 2024. doi: 10.1007/978-1-0716-3561-2_14.
- [30] Sutoyo E., Almaarif A., Yanto I.T.R.: Sentiment Analysis of Student Evaluationsof Teaching Using Deep Learning Approach. In: J.H. Abawajy, K.K.R. Choo,H. Chiroma (eds.), International Conference on Emerging Applications and Technologies for Industry 4.0 (EATI’2020), Lecture Notes in Networks and Systems, vol. 254, pp. 272–281, Springer, Cham, 2021. doi: 10.1007/978-3-030-80216-5_20.
- [31] Tzacheva A.A., Easwaran A.: Emotion Detection and Opinion Mining from Student Comments for Teaching Innovation Assessment, International Journal of Education, vol. 9(2), pp. 21–32, 2021. doi: 10.5121/ije2021.9203.
- [32] Yu L.C., Lee C.W., Pan H.I., Chou C.Y., Chao P.Y., Chen Z.H., Tseng S.F.,et al.: Improving early prediction of academic failure using sentiment analysison self-evaluated comments, Journal of Computer Assisted Learning, vol. 34(4), pp. 358–365, 2018. doi: https://doi.org/10.1111/jcal.12247.
- [33] Zhang Y., Sun S., Galley M., Chen Y.C., Brockett C., Gao X., Gao J.,et al.: DIALOGPT: Large-Scale Generative Pretraining for Conversational Response Generation. In: A. Celikyilmaz, T.H. Wen (eds.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 270–278, Association for Computational Linguistics, 2020. doi: 10.18653/v1/2020.acl-demos.30.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8454327b-c000-42a2-8863-744120300f3d
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