E-learning and sentiment analysis: a case study

E-Learning is becoming one of the most effective training approaches. In particular, the blended learning is considered a useful methodology for supporting and understanding students and their learning issues. Thanks to e-Learning platforms and their collaborative tools, students can interact with other students and share doubts on certain topics. However, teachers often remain outside of this process and do not understand the learning problems that are in their classrooms. A solution for ensuring the privacy of communication among students could be the adoption of a Sentiment Analysis methodology for the detection of the classroom mood during the learning process. In this paper, we investigate the adoption of a probabilistic approach based on the Latent Dirichlet Allocation (LDA) as Sentiment Grabber. The proposed approach can detect the mood of students on the various topics and teacher can better tune his/her teaching approach. The proposed method has been tested in real cases with effective and satisfactory results.

[1]  Navneet Kaur,et al.  Opinion mining and sentiment analysis , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[2]  Janyce Wiebe,et al.  Just How Mad Are You? Finding Strong and Weak Opinion Clauses , 2004, AAAI.

[3]  Michael Gamon,et al.  Automatic Identification of Sentiment Vocabulary: Exploiting Low Association with Known Sentiment Terms , 2005, ACL 2005.

[4]  Xiangji Huang,et al.  Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain , 2012, IEEE Transactions on Knowledge and Data Engineering.

[5]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[6]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[7]  Paolo Napoletano,et al.  A Query Expansion Method based on a Weighted Word Pairs Approach , 2013, IIR.

[8]  Yao Sun,et al.  Processing Continuous Queries on Sensor-Based Multimedia Data Streams by Multimedia Dependency Analysis and Ontological Filtering , 2011, Int. J. Softw. Eng. Knowl. Eng..

[9]  Antonio Garrido,et al.  E-Learning and Intelligent Planning: Improving Content Personalization , 2014, IEEE Revista Iberoamericana de Tecnologias del Aprendizaje.

[10]  Sorel Reisman The Future of Online Instruction, Part 2 , 2014, Computer.

[11]  Michael L. Littman,et al.  Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus , 2002, ArXiv.

[12]  Daniel McDuff,et al.  Measuring Voter's Candidate Preference Based on Affective Responses to Election Debates , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[13]  Rosa M. Carro,et al.  Sentiment analysis in Facebook and its application to e-learning , 2014, Comput. Hum. Behav..

[14]  Richard Colbaugh,et al.  Estimating sentiment orientation in social media for intelligence monitoring and analysis , 2010, 2010 IEEE International Conference on Intelligence and Security Informatics.

[15]  Yolaine Bourda,et al.  Expressing Adaptation Strategies Using Adaptation Patterns , 2012, IEEE Transactions on Learning Technologies.

[16]  Sorel Reisman The Future of Online Instruction, Part 1 , 2014, Computer.

[17]  Mitsuru Ishizuka,et al.  SentiFul: A Lexicon for Sentiment Analysis , 2011, IEEE Transactions on Affective Computing.

[18]  Shrikanth S. Narayanan,et al.  A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle , 2012, ACL.

[19]  Mahmoud Neji,et al.  The Affective Tutoring System , 2010, Expert Syst. Appl..

[20]  Pilar Rodríguez Marín,et al.  Extracting Emotions from Texts in E-Learning Environments , 2012, 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems.

[21]  Thomas Hofmann,et al.  Probabilistic Latent Semantic Analysis , 1999, UAI.

[22]  Noriko Hara,et al.  Students’ Distress with a Web-based Distance Education Course: An Ethnographic Study of Participants' Experiences , 2003 .

[23]  Haixun Wang,et al.  Semantic Multidimensional Scaling for Open-Domain Sentiment Analysis , 2014, IEEE Intelligent Systems.

[24]  Paolo Napoletano,et al.  Text classification using a few labeled examples , 2014, Comput. Hum. Behav..

[25]  Vidyasagar Potdar,et al.  A state of the art opinion mining and its application domains , 2009, 2009 IEEE International Conference on Industrial Technology.

[26]  Marco Baroni,et al.  Identifying subjective adjectives through web-based mutual information , 2004 .

[27]  Bing Liu,et al.  Sentiment Analysis and Subjectivity , 2010, Handbook of Natural Language Processing.

[28]  Francisco J. García-Peñalvo,et al.  Development of e-Learning Solutions: Different Approaches, a Common Mission , 2014, IEEE Revista Iberoamericana de Tecnologias del Aprendizaje.

[29]  Rudy Prabowo,et al.  Sentiment analysis: A combined approach , 2009, J. Informetrics.

[30]  Francesco Colace,et al.  A Probabilistic Approach to Tweets' Sentiment Classification , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[31]  Haji Binali,et al.  A new significant area: Emotion detection in E-learning using opinion mining techniques , 2009, 2009 3rd IEEE International Conference on Digital Ecosystems and Technologies.

[32]  Hongfei Lin,et al.  Opinion Mining in e-Learning System , 2007, 2007 IFIP International Conference on Network and Parallel Computing Workshops (NPC 2007).

[33]  Min Zhang Proceedings of the ACL 2012 System Demonstrations , 2012 .