Potential Applications of Sentiment Analysis in Educational Research and Practice – Is SITE the Friendliest Conference?
PROCEEDINGS
Matthew Koehler, Spencer Greenhalgh, Andrea Zellner, Michigan State University, United States
Society for Information Technology & Teacher Education International Conference, in Las Vegas, NV, United States ISBN 978-1-939797-13-1 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC USA
Abstract
Despite the widespread use of sentiment analysis by many disciplines, it has been a largely underused tool in educational contexts. The purpose of this paper is to explore some potential uses for sentiment analysis in educational settings and to present a sample study using the approach. Using sentiment analysis, we compare the “friendliness” of two educational technology conferences and use these data to answer the question: Is SITE the friendliest conference? We then expand the discussion to consider how education researchers and practitioners may fruitfully use sentiment analysis.
Citation
Koehler, M., Greenhalgh, S. & Zellner, A. (2015). Potential Applications of Sentiment Analysis in Educational Research and Practice – Is SITE the Friendliest Conference?. In D. Rutledge & D. Slykhuis (Eds.), Proceedings of SITE 2015--Society for Information Technology & Teacher Education International Conference (pp. 1348-1354). Las Vegas, NV, United States: Association for the Advancement of Computing in Education (AACE). Retrieved March 19, 2024 from https://www.learntechlib.org/primary/p/150179/.
© 2015 Association for the Advancement of Computing in Education (AACE)
Keywords
References
View References & Citations Map- Baucom, E., Sanjari, A., Liu, X., & Chen, M. (2013). Mirroring the real world in social media: Twitter, geolocation, and sentiment analysis. In Proceedings of the 2013 International Workshop on Mining Unstructured Big Data using Natural Language Processing (pp. 61-68). New York: ACM Press.
- Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8.
- Bollen, J., Pepe, A., & Mao, H. (2009). Modeling public mood and emotion: Twitter sentiment and socioeconomic phenomena. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (pp. 450453).
- Ceron, A., Curini, L., & Iacus, S.M. (2015). Using sentiment analysis to monitor electoral campaigns: Method matters—evidence from the United States and Italy. Social Science Computer Review, 33(1), 3-20.
- Das, S., & Chen, M. (2001). Yahoo! for Amazon: Extracting market sentiment from stock message boards. In Proceedings of the 8th AsiaPacific Finance Association Annual Conference (APFA) (pp. 37-56).
- Eckert, P. (2006). Communities of practice. Encyclopedia of language and linguistics, 2, 683-685.
- Graesser, A.C., McNamara, D.S., & Kulikowich, J.M. (2011). Coh-Metrix: Providing multilevel analyses of text characteristics. Educational Researcher, 40(5), 223–234.
- Hongwei, W. (2012). Coh-Metrix: A computational tool to discriminate writing qualities. International Education Studies, 5(2), 204–215.
- Koehler, M.J. (2015). #Tpack Mood tracker [website]. Retreived from http://www.mattkoehler.com/Sentiment/graph.php?days=all & Tag=tpack
- Koehler, M., & Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)? Contemporary Issues in Technology and Teacher Education, 9(1), 60-70.
- McNamara, D.S., & Graesser, A.C. (2011). Coh-Metrix: An automated tool for theoretical and applied natural language processing. In Applied natural language processing: Identification investigation and resolution (pp. 188–
- Mishra, P., & Koehler, M. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. The Teachers College Record, 108(6), 1017-1054.
- O’Connor, B., Balasubramanyan, R., Routledge, B.R., & Smith, N.A. (2010). From tweets to polls: Linking text sentiment to public opinion time series. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (pp. 122–129). Menlo Park, CA: AAAI Press.
- Ortigosa, A., Martín, J.M., & Carro, R.M. (2014). Sentiment analysis in Facebook and its application to e-learning. Computers in Human Behavior, 31, 527-541.
- Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1 & 2), 1-135.
- Perelman, L. (2014). When “the state of the art” is counting words. Assessing Writing, 21, 104–111.
- Shermis, M.D. (2014). State-of-the-art automated essay scoring: Competition, results, and future directions from a United States demonstration. Assessing Writing, 20, 53–76.
- Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62(2), 406-418.
- Tong, R.M. (2001). An operational system for detecting and tracking opinions in on-line discussion. In Proceedings of the Workshop on Operational Text Classification (P. 6).
- Wang, C.J., Wang, P.P., & Zhu, J.J. (2013). Discussing Occupy Wall Street on Twitter: Longitudinal network analysis of equality, emotion, and stability of public discussion. Cyberpsychology, Behavior, and Social Networking, 16(9), 679-685.
These references have been extracted automatically and may have some errors. Signed in users can suggest corrections to these mistakes.
Suggest Corrections to References