Sentiment Analysis: Towards a Tool for Analysing Real-Time Students Feedback

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn 2014-December, Issue , 2014, Pages 419-423

  Altrabsheh, Nabeela ^a   Cocea, Mihaela ^a   Fallahkhair, Sanaz ^a  

^a UNIVERSITY OF PORTSMOUTH   (United Kingdom)

Author keywords

Educational Data Mining; Feature Selection; Real time Feedback; Sentiment Analysis

Indexed keywords

ARTIFICIAL INTELLIGENCE; EDUCATION; EDUCATION COMPUTING; FEATURE EXTRACTION; LEARNING SYSTEMS; STUDENTS; SUPPORT VECTOR MACHINES;

AUTOMATIC ANALYSIS; BEST MODEL; EDUCATIONAL DATA MINING; MACHINE LEARNING TECHNIQUES; REAL TIME; REAL-TIME FEEDBACK; SENTIMENT ANALYSIS;

DATA MINING;

EID: 84946572262     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2014.70     Document Type: Conference Paper

References (15)

1. J. Novak and M. Cowling, "The implementation of social networking as a tool for improving student participation in the classroom," ISANA International Academy Association Conference Proceedings, vol. 22, pp. 1-10, 2011.
2. A. Go, R. Bhayani, and L. Huang. (2009) Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford. [Online]. Available: http://s3.eddieoz.com/docs/sentiment analysis/Twitter Sentiment Classification using Distant Supervision
3. F. Tian, Q. Zheng, R. Zhao, T. Chen, and X. Jia, "Can e-learner's emotion be recognized from interactive Chinese texts" International Conference on Computer Supported Cooperative Work in Design, vol. 13, pp. 546-551, 2009.
4. A. Ortigosa, J. M. Martin, and R. M. Carro, "Sentiment analysis in Facebook and its application to e-learning," Computers in Human Behavior, vol. 31, pp. 527-541, 2014.
5. M. Munezero, C. S. Montero, M. Mozgovoy, and E. Sutinen, "Exploiting sentiment analysis to track emotions in students' learning diaries," Koli Calling International Conference on computing Education Research, vol. 13, pp. 145-152, 2013.
6. C. Troussas, M. Virvou, K. Junshean Espinosa, K. Llaguno, and J. Caro, "Sentiment analysis of facebook statuses using naive bayes classifier for language learning," Information, Intelligence, Systems and Applications, vol. 4, pp. 1-6, 2013.
7. J. Martin, A. Ortigosa, and R. Carro, "Sentbuk: Sentiment analysis for e-learning environments," International Symposium on Computers in Education (SIIE), vol. 12, pp. 212-217, Oct 2012.
8. A. Agarwal, B. Xie, I. Vovsha, O. Rambow, and R. Passonneau, "Sentiment analysis of twitter data," in Proceedings of the Workshop on Languages in Social Media, Stroudsburg, PA, USA, 2011, pp. 30-38.
9. W. Wang and J. Wu, "Emotion recognition based on cso&svm in e-learning," International Conference on Natural Computation (ICNC), vol. 7, pp. 566-570, 2011.
10. A. Pak and P. Paroubek, "Twitter based system: Using twitter for disambiguating sentiment ambiguous adjectives," International Workshop on Semantic Evaluation, vol. 5, pp. 436-439, 2010.
11. B. Pang and L. Lee, "A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts," Annual Meeting on Association for Computational Linguistics, vol. 42, pp. 271-278, 2004.
12. D. Song, H. Lin, and Z. Yang, "Opinion mining in e-learning system," International Conference on Network and Parallel Computing Workshops, vol. 6, pp. 788-792, Sept 2007.
13. B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs up: Sentiment classification using machine learning techniques," ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79-86, 2002.
14. S. Jain and S. Nayak. (2013) Sentiment analysis of movie reviews: A study of features and classifiers. CS221 Project Report, Stanford. [Online]. Available: http://web.stanford.edu/nayaks/reportsFolder/cs221 report
15. B. Gokulakrishnan, P. Priyanthan, T. Ragavan, N. Prasath, and A. Perera, "Opinion mining and sentiment analysis on a twitter data stream," Advances in ICT for Emerging Regions (ICTer), vol. 13, pp. 182-188, Dec 2012.