EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 297 (2021) E3S Web Conf., 297 (2021) 01010 References
Open Access
Issue
E3S Web Conf.
Volume 297, 2021
The 4th International Conference of Computer Science and Renewable Energies (ICCSRE'2021)
Article Number 01010
Number of page(s) 7
DOI https://doi.org/10.1051/e3sconf/202129701010
Published online 22 September 2021
  1. N. Altrabsheh, M. Cocea & S. Fallahkhair Predicting learning-related emotions from students’ textual classroom feedback via Twitter, In The 8th International Conference on Educational Data Mining (EDM) (pp. 436–440) (2015). [Google Scholar]
  2. J. Sultana, N. Sultana, K. Yadav & F. AlFayez, Prediction of Sentiment Analysis on Educational Data based on Deep Learning Approach. in 2018 21st Saudi Computer Society National Computer Conference (NCC) 1–5 (IEEE, 2018). [Google Scholar]
  3. P. Rodriguez, A. Ortigosa & R. M. Carro Detecting and making use of emotions to enhance student motivation in e-learning environments. International Journal of Continuing Engineering Education and Life Long Learning 24, 168–183 (2014). [Google Scholar]
  4. Y. Madani, H. Ezzikouri, M. Erritali & B. Hssina Finding optimal pedagogical content in an adaptive e-learning platform using a new recommendation approach and reinforcement learning. J Ambient Intell Human Comput 11, 3921–3936 (2020). [Google Scholar]
  5. M. Birjali, A. Beni-Hssane & M. Erritali A novel adaptive e-learning model based on Big Data by using competence-based knowledge and social learner activities. Applied Soft Computing 69, 14–32 (2018). [Google Scholar]
  6. P. Cen, K. Zhang & D. Zheng Sentiment Analysis Using Deep Learning Approach. Journal on Artificial Intelligence 2, 17–27 (2020). [Google Scholar]
  7. M. L. B. Estrada, R. Z. Cabada, O. R. Bustillos & M. Graff. Opinion mining and emotion recognition applied to learning environments. Expert Systems with Applications 150, 113265 (2020). [Google Scholar]
  8. M. S. Kim, & R. A. Calvo Sentiment analysis in student experiences of learning. In Educational Data Mining. (2010). [Google Scholar]
  9. G. Xu, Y. Meng, X. Qiu, Z. Yu & X. Wu Sentiment Analysis of Comment Texts Based on BiLSTM. IEEE Access 7, 51522–51532 (2019). [Google Scholar]
  10. Z. H. Kilimci & S. Akyokus Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification. Complexity vol. 2018 e7130146 (2018). [Google Scholar]
  11. A. Agrawal & A. An Unsupervised Emotion Detection from Text Using Semantic and Syntactic Relations. in 2012IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology 346–353 (IEEE, 2012). [Google Scholar]
  12. C. O. Alm, D. Roth, & R. Sproat, Emotions from text: machine learning for text-based emotion prediction. Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT ‘05. 1220648 (2005). [Google Scholar]
  13. Z. Teng, F. Ren & S. Kuroiwa Emotion Recognition from Text based on the Rough Set Theory and the Support Vector Machines. in 2007 International Conference on Natural Language Processing and Knowledge Engineering 36–41 (IEEE, 2007). [Google Scholar]
  14. N. Altrabsheh, M. Cocea & S. Fallahkhair Learning Sentiment from Students’ Feedback for Real-Time Interventions in Classrooms. in Adaptive and Intelligent Systems (ed. Bouchachia, A.) vol. 8779 40–49 (Springer International Publishing, 2014). [Google Scholar]
  15. A. Ortigosa, J. M. Martín & R. M. Carro Sentiment analysis in Facebook and its application to e-learning. Computers in Human Behavior 31, 527–541 (2014). [Google Scholar]
  16. L. C. Yu, et al. Improving early prediction of academic failure using sentiment analysis on self-evaluated comments. J Comput Assist Learn 34, 358–365 (2018). [Google Scholar]
  17. J. Pang et al. Fast Supervised Topic Models for Short Text Emotion Detection. IEEE Trans. Cybern. 51, 815–828 (2021). [CrossRef] [PubMed] [Google Scholar]
  18. X. Hu, J. Tang, H. Gao & H. Liu Unsupervised sentiment analysis with emotional signals. in Proceedings of the 22nd international conference on World Wide Web 607–618 (Association for Computing Machinery, 2013). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.