Sentiment analysis in student experiences of learning

Educational Data Mining 2010 - 3rd International Conference on Educational Data Mining

Volumn , Issue , 2010, Pages 111-120

  MacKim, Sunghwan ^a   Calvo, Rafael A ^a  

^a UNIVERSITY OF SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

CATEGORICAL MODEL; DIMENSIONAL MODEL; ENGLISH WORD; LATENT SEMANTIC ANALYSIS; LEXICAL RESOURCES; NEGATIVE POLARITY; NONNEGATIVE MATRIX FACTORIZATION; POSITIVE POLARITY; PREDICTION MODEL; SENTIMENT ANALYSIS; TWO DIMENSIONALITY;

BLIND SOURCE SEPARATION; EDUCATION; MATHEMATICAL MODELS;

DATA MINING;

EID: 84857468067     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. ITL. Purpose of the USE 2008 [cited 2009 February 10]; Available from: http://www.itl.usyd.edu.au/use/purpose.htm.
2. Marsh, H.W., Students' evaluations of University teaching: Research findings, methodological issues, and directions for future research. International Journal of Educational Research, 1987. 11(3): p. 253-388.
3. Richardson, J.T.E., Instruments for obtaining student feedback: a review of the literature. Assessment & Evaluation in Higher Education, 2005. 30(4): p. 387-415.
4. Pang, B. and L. Lee, Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2008. 2(1-2): p. 1-135.
5. Ekman, P., An Argument for Basic Emotions. 1992. p. 200.
6. D'Mello, S., R. Picard, and A. Graesser, Toward an Affect-Sensitive AutoTutor. IEEE Intelligent Systems, 2007. 22(4): p. 53-61. (Pubitemid 47274706)
7. Russell, J.A., Core affect and the psychological construction of emotion. Psychological Review, 2003. 110(1): p. [Washington, etc.] American Psychological Association [etc.]-172.
8. Lang and P.J. (1980). "Behavioral treatment and bio-behavioral assessment: Computer applications." Technology in mental health care delivery systems: 119-137.
9. Strapparava, C. and R. Mihalcea. Learning to identify emotions in text. in Proceedings of the 2008 ACM symposium on Applied computing. 2008.
10. Penumatsa, P., M. Ventura, et al. (2006). The Right Threshold Value: What Is the Right Threshold of Cosine Measure When Using Latent Semantic Analysis for Evaluating Student Answers? International Journal on Artificial Intelligence Tools, WORLD SCIENTIFIC PUBLISHING.
11. D'Mello, S., et al. Automatic Detection of Learner's Affect from Conversational Cues. in User Modeling and User-Adapted Interaction. 2008.
12. Landauer, T.K., et al., eds. Handbook of Latent Semantic Analysis. 2007, Routledge.
13. Lee, D.D. and H.S. Seung, Learning the parts of objects by non-negative matrix factorization. Nature, 1999. 401(6755): p. 788-791.
14. Bradley, M. M. and P. J. Lang (1999). "Affective norms for English words (ANEW): Instruction manual and affective ratings." University of Florida: The Center for Research in Psychophysiology.
15. Zeimpekis, D. and E. Gallopoulos, TMG: A MATLAB toolbox for generating term-document matrices from text collections. Grouping multidimensional data: Recent advances in clustering, 2005: p. 187-210.
16. He, H. and E.A. Garcia, Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 2009. 21(9): p. 1263.