Lopa MANDAL, Samar BHATTACHARYA, Pramatha Nath BASU, Rohan DAS

Intellimote: a hybrid classifier for classifying learners' emotion in a distributed e-learning environment

A huge collection of textual, graphical, audio, and video contents are readily available on the Internet to be used for the purpose of learning. Sentimental feedbacks of learners posted at the end of many of these contents may be considered as genuine reactions of the learners who have gone through the contents. Such learners sentiments are important inputs for judging the acceptability of a learning material. Analyzing such feedbacks using sentiment analysis techniques can identify the best reusable learning contents that may be used for developing new courseware. This can significantly reduce the time and effort of authoring, which is otherwise a difficult, time-consuming, and costly affair. This methodology can also be used for continuous assessment on the learning materials released for use. This paper presents a machine learning-based approach for emotion analysis in e-learning materials. It describes the design and experimental use of a sentiment analysis classifier that uses classifier combination rules to combine polarity scoring and a support vector machine (SVM). The present approach also gives an opportunity for users to train a lexicon on a very specialized set of data, pertaining to the domain of usage. This helps either to enhance polarity scores of certain words that appear more frequently or to add words that are completely missing from the lexicon, and contributes a great deal in determining the polarity scores.

PDF

___

[1] Wang C, Xiao Z, Liu Y, Xu Y, Zhou A, Zhang K. SentiView: Sentiment analysis and visualization for internet popular topics. IEEE T Hum Mach Syst 2013; 43: 620-630.
[2] Pang B, Lee L. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2008; 2: 1-35.
[3] Turney PD. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: 40th Annual Meeting on Association for Computational Linguistics; 6 Jul 2002; Association for Computational Linguistics. pp. 417-424.
[4] Wilson T, Wiebe J, Hwa R. Just how mad are you? Finding strong and weak opinion clauses. In: AAAI 2004 21st Conference of the American Association for Artificial Intelligence; Jul 25 2004; San Jose, CA, USA. AAAI. vol. 4. pp. 761-769.
[5] Baroni M, Vegnaduzzo S. Identifying subjective adjectives through web-based mutual information. In: KONVENS; Sep 2004. vol. 4. pp. 17-24.
[6] Turney P, Littman ML. Unsupervised learning of semantic orientation from a hundred-billion-word corpus. Technical report NRC technical report ERB-1094. Institute for Information Technology, National Research Council, Canada. 2002.
[7] Gamon M, Aue A. Automatic identification of sentiment vocabulary: exploiting low association with known sentiment terms. In: ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing; Jun 29 2005. pp. 57-64.
[8] Pang B, Lee L, Vaithyanathan S. Thumbs up?: sentiment classification using machine learning techniques. In: ACL 2002 Conference on Empirical Methods in Natural Language Processing; Jul 6 2002. vol. 10. pp. 79-86.
[9] Prabowo R, Thelwall M. Sentiment analysis: A combined approach. J. Informetrics. 2009; 3: 143-157.
[10] Shein KP. Ontology based combined approach for sentiment classification. In: 3rd International Conference on Communications and information technology; 29 Dec 2009; World Scientific and Engineering Academy and Society (WSEAS). pp. 112-115.
[11] Colbaugh R, Glass K. Estimating sentiment orientation in social media for intelligence monitoring and analysis. In: IEEE 2010 International Conference on Intelligence and Security Informatics (ISI); 23 May 2010. pp. 135-137.
[12] Esuli A, Sebastiani F. Sentiwordnet: A publicly available lexical resource for opinion mining. In: LREC 2006; 22 May 2006. vol. 6. pp. 417-422.
[13] Wang H, Xu H, Liu L, Song W, Du C. An unsupervised microblog emotion dictionary construction method and its application on sentiment analysis. Journal of Information & Computational Science 2015; 12: 2729-2739.
[14] Stone PJ, Dunphy DC, Smith MS. The General Inquirer: A Computer Approach to Content Analysis. Cambridge, MA, USA: MIT Press, 1966.
[15] Whissell C. Using the revised dictionary of affect in language to quantify the emotional undertones of samples of natural language. Psychol Rep 2009; 105: 509-21.
[16] Miller G, Fellbaum C. Wordnet: An Electronic Lexical Database. Cambridge, MA, USA: MIT Press, 1998.
[17] Poria S, Gelbukh A, Cambria E, Hussain A, Huang GB. EmoSenticSpace: A novel framework for affective commonsense reasoning. Knowledge-Based Systems 2014; 69: 108-123.
[18] Li W, Xu H. Text-based emotion classification using emotion cause extraction. Expert Syst Appl 2014; 41: 1742- 1749.
[19] Bravo-Marquez F, Mendoza M, Poblete B. Meta-level sentiment models for big social data analysis. KnowledgeBased Systems 2014; 69: 86-99.
[20] Jian C, Gao J, Ao Y. A new sampling method for classifying imbalanced data based on support vector machine ensemble. Neurocomputing 2016; 193: 115-122.
[21] Peker M. A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM. J Med Syst 2016; 40: 1-16.