Peerumporn Jiranantanagorn
Haifeng Shen
ABSTRACT
Digital backchannel systems have been proven useful to help a lecturer gather real-time online feedback from students in a lecture environment. However, the large number of posts made during a lecture creates a major hurdle for the lecturer to promptly analyse them and take actions accordingly in time. To tackle this problem, we propose a solution that analyses the sentiment of students' feedback and visualises the morale trend of the student population to the lecturer in real time. In this paper, we present the user interface for morale visualisation and playback of ranked posts as well as the techniques for sentiment analysis and morale computation.
- Antoine, J.-Y., J. Villaneau and A. Lefeuvre (2014). Weighted Krippendorff's alpha is a more reliable metrics for multi- coders ordinal annotations: experimental studies on emotion, opinion and coreference annotation. EACL 2014, Gotenborg, Sweden.Google Scholar
Cross Ref
- Baccianella, S., A. Esuli and F. Sebastiani (2010). SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. LREC.Google Scholar
- Brooke, J. (1996). "SUS-A quick and dirty usability scale." Usability evaluation in industry 189(194): 4--7.Google Scholar
- Chickering, A. W. and Z. F. Gamson (1987). "Seven principles for good practice in undergraduate education." AAHE bulletin 3: 7.Google Scholar
- Davis, F. D. (1989). "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology." MIS Quarterly 13(3): 319--340.Google ScholarDigital Library
- Diakopoulos, N., M. Naaman and F. Kivran-Swaine (2010). Diamonds in the rough: Social media visual analytics for journalistic inquiry. 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST). Google ScholarCross Ref
- Dork, M., D. Gruen, C. Williamson and S. Carpendale (2010). "A Visual Backchannel for Large-Scale Events." IEEE Transactions on Visualization and Computer Graphics 16(6): 1129--1138.Google ScholarDigital Library
- Faridani, S., E. Bitton, K. Ryokai and K. Goldberg (2010). Opinion space: a scalable tool for browsing online comments. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Atlanta, Georgia, USA, ACM: 1175--1184.Google ScholarDigital Library
- Guzman, E. and B. Bruegge (2013). Towards emotional awareness in software development teams. Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering. Saint Petersburg, Russia, ACM: 671--674.Google ScholarDigital Library
- Healey, C. and J. Enns (2012). "Attention and Visual Memory in Visualization and Computer Graphics." IEEE Transactions on Visualization and Computer Graphics 18(7): 1170--1188.Google ScholarDigital Library
- Jiranantanagorn, P., H. Shen, R. Goodwin and K.-K. Teoh (2015). "ClasSense: A Mobile Digital Backchannel System for Monitoring Class Morale." International Journal of Learning and Teaching 1(2): 161--167.Google Scholar
- Kellogg, W. A., T. Erickson, T. V. Wolf, S. Levy, J. Christensen, J. Sussman and W. E. Bennett (2006). Leveraging digital backchannels to enhance user experience in electronically mediated communication. Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work. Banff, Alberta, Canada, ACM: 451--454.Google ScholarDigital Library
- Krippendorff, K. (2004). Content analysis: An introduction to its methodology, Sage.Google Scholar
- Lombard, M., J. Snyder-Duch and C. Bracken (2010). "Intercoder reliability." Retrieved February 3: 2011.Google Scholar
- Marcus, A., M. S. Bernstein, O. Badar, D. R. Karger, S. Madden and R. C. Miller (2011). Twitinfo: aggregating and visualizing microblogs for event exploration. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Vancouver, BC, Canada, ACM: 227--236.Google ScholarDigital Library
- Pang, B. and L. Lee (2008). "Opinion Mining and Sentiment Analysis." Foundations and Trends in Information Retrieval 2(1--2): 1--135.Google Scholar
- Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot and É. Duchesnay (2011). "Scikit-learn: Machine Learning in Python." J. Mach. Learn. Res. 12: 2825--2830.Google ScholarDigital Library
- Ruipérez-Valiente, J. A., P. J. Muñoz-Merino, D. Leony and C. Delgado Kloos (2015). "ALAS-KA: A learning analytics extension for better understanding the learning process in the Khan Academy platform." Computers in Human Behavior 47: 139--148.Google ScholarDigital Library
- Sopan, A., P. J. Rey, B. Butler and B. Shneiderman (2012). Monitoring Academic Conferences: Real-Time Visualization and Retrospective Analysis of Backchannel Conversations. 2012 International Conference on Social Informatics. Google ScholarDigital Library
- Spence, R. (2007). Information Visualization: Design for Interaction (2nd Edition), Prentice-Hall, Inc.Google Scholar
- Sylwester, R. (1994). "How emotions affect learning." Educational Leadership 52(2): 60--65.Google Scholar
- Tausczik, Y. R. and J. W. Pennebaker (2010). "The psychological meaning of words: LIWC and computerized text analysis methods." Journal of Language and Social Psychology 29(1): 24--54.Google ScholarCross Ref
- Thelwall, M. (2013). "Heart and soul: Sentiment strength detection in the social web with sentistrength." Proceedings of the CyberEmotions: 1--14.Google Scholar
- Thelwall, M., K. Buckley, G. Paltoglou, D. Cai and A. Kappas (2010). "Sentiment strength detection in short informal text." Journal of the American Society for Information Science and Technology 61(12): 2544--2558.Google ScholarCross Ref
- Ware, C. (2012). Information visualization: perception for design, Elsevier.Google Scholar
Index Terms
- Sentiment analysis and visualisation in a backchannel system
Recommendations
Designing a Mobile Digital Backchannel System for Monitoring Sentiments and Emotions in Large Lectures
ASWEC ' 15 Vol. II: Proceedings of the ASWEC 2015 24th Australasian Software Engineering ConferenceDigital backchannel systems have been proven useful for gathering real-time students' feedback in a large lecture environment. However, it is difficult for lecturers to process and respond to large amount of feedback in real time while they are teaching ...
Followee recommendation based on topic extraction and sentiment analysis from tweets
iiWAS '15: Proceedings of the 17th International Conference on Information Integration and Web-based Applications & ServicesTwitter has become a popular social media service, accumulating and distributing vast amounts of information for its numerous users. One feature of Twitter is that it enables a user to follow other users, who can obtain the information her/his followees ...
Role of Emoticons for Multidimensional Sentiment Analysis of Twitter
iiWAS '14: Proceedings of the 16th International Conference on Information Integration and Web-based Applications & ServicesMicroblogging systems such as Twitter and Facebook have become popular. People can easily post their sentiments to the Internet in real time using such microblogging systems. Twitter is a text-based communication tool. Users cannot use non-verbal ...
Comments