Myriam Peñafiel
Sergio Luján-Mora
ABSTRACT
The use of the web as a universal communication platform generates large volumes of data (Big data), which in many cases, need to be processed so that they can become useful knowledge in face of the sceptics who have doubts about the credibility of such information. The use of web data that comes from educational contexts needs to be addressed, since that large amount of unstructured information is not being valued, losing valuable information that can be used. To solve this problem, we propose the use of data mining techniques such as sentiment analysis to validate the information that comes from the educational platforms. The objective of this research is to propose a methodology that allows the user to apply sentiment analysis in a simple way, because although some researchers have done it, very few do with data in the educational context. The results obtained prove that the proposal can be used in similar cases.
- Nanli, Z., Ping Z., Weiguo, L. and Meng, C. 2012. Sentiment analysis: A literature review. In International Symposium on Management of Technology (ISMOT), 572--576.Google Scholar
- Warren-Payne, A. 2016. 13 epic stats and facts from The State of Social webinar. https://www.clickz.com/13-epic-stats-and-facts-from-the-state-of-social-webinar/110510/2016Google Scholar
- Statista. 2017. The Statistics Portal, https://goo.gl/6fdPTuGoogle Scholar
- Kaplan, A. and Haenlein, M. 2010. Users of the world, unite! The challenges and opportunities of Social Media, Business horizons, 53, 1, 59--68.Google Scholar
- Cavazza, F. 2018. Social Media Landscape. https://fredcavazza.net/G.Google Scholar
- You, Q. 2016. Sentiment and Emotion Analysis for Social Multimedia: Methodologies and Applications. In Proceedings of the 2016 ACM on Multimedia Conference (MM '16). ACM, New York, NY, USA, 1445--1449. Google ScholarDigital Library
- Gandomi, A. and Haider M. 2015. Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35, 2, 137--144. Google ScholarDigital Library
- Liu, B. 2012. Sentiment Analysis and Opinion Mining: Synthesis Lectures on Human Language Technologies. San Rafael: Morgan & Claypool Publishers.Google Scholar
- Del-Vicario, M., Zolloa, F., Caldarellia, G., Scalab, A. and Quattrociocchia, W. 2017.Mapping social dynamics on Facebook: The Brexit debate. Social Networks, 50, 6--16.Google ScholarCross Ref
- Immigration, Refugees and Citizenship Canada (IRCC). 2017. https://twitter.com/CitImmCanadaGoogle Scholar
- Miller, L. D., Soh, L. K., Samal, A., Kupzyk K. and Nugent, G. A. 2015. Comparison of Educational Statistics and Data Mining Approaches to Identify Characteristics that Impact Online Learning. JEDM-Journal of Educational Data Mining, 7, 3, 117--150.Google Scholar
- Almasoud, A. M., Al-Khalifa, H. S. and Al-Salman, A. 2015. Recent developments in data mining applications and techniques. In 2015 Tenth International Conference on Digital Information Management (ICDIM), 36--42.Google Scholar
- Zaldumbide, J. and Sinnott, R. O. 2015. Identification and Validation of Real-Time Health Events through Social Media. In 2015 IEEE International Conference on Data Science and Data Intensive Systems, 9-16. Google ScholarDigital Library
- Cai, K., Spangler, S., Chen, Z and Zhang, L. 2008. Leveraging Sentiment Analysis for Topic Detection. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 (WI-IAT '08), 1. IEEE Computer Society, Washington, DC, USA, 265--271. Google ScholarDigital Library
- Feldman, R. 2013. Techniques and applications for sentiment analysis. Commun. ACM 56, 4 (April 2013), 82--89. Google ScholarDigital Library
- Souza, E., Vitório, D., Castro, D., Oliveira, A. and Gusmão, C. 2016. Characterizing Opinion Mining: A Systematic Mapping Study of the Portuguese Language. In International Conference on Computational Processing of the Portuguese Language (PROPOR), 122--127.Google Scholar
- Liu, Y., Yu, X., Chen, Z. and Liu. B 2013. Sentiment analysis of sentences with modalities. In Proceedings of the 2013 international workshop on Mining unstructured big data using natural language processing (UnstructureNLP '13). ACM, New York, NY, USA, 39--44. Google ScholarDigital Library
- Martínez-Cámara, E., Martín-Valdivia, M.T., Ureña López, L. A. and Mitkov, R. 2015. Polarity classification for Spanish tweets using the COST corpus, Journal of Information Science, 41, 3, 263--272. Google ScholarDigital Library
- Hutto, J. and Gilbert, E. 2014. Vader: A parsimonious rule-based model for sentiment analysis of social media text, In Eighth International AAAI Conference on Weblogs and Social Media.Google Scholar
- Estevao da Silva, L. A. 2016. A Data Mining Approach for Standardization of Collectors Names in Herbarium Database. IEEE Latin America Transactions, 14, 2, 805--810.Google ScholarCross Ref
- Peñafiel, M., Lujan-Mora, S., Vintimilla, LM. and Pozo P. 2015. Analysis of the usage of virtual classrooms in the National Polytechnic School of Ecuador: Teachers' perception, In Information Technology Based Higher Education and Training (ITHET), Lisbon, PT, 1--6.Google Scholar
- Peñafiel, M., Vásquez, S. and Lujan-Mora, S. 2016. Use of Virtual Classroom: Summarized Opinion of the Stakeholders in the Learning-Teaching Process, In Information Technology Based Higher Education and Training (CSEDU), Rome, IT, 314--320. Google ScholarDigital Library
Index Terms
- Data Mining and Opinion Mining: A Tool in Educational Context
Recommendations
Current State of Text Sentiment Analysis from Opinion to Emotion Mining
Sentiment analysis from text consists of extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. It is often equated to opinion mining, but it should also encompass emotion mining. Opinion ...
Evaluation of an Educational Training Platform Using Text Mining
SETN '18: Proceedings of the 10th Hellenic Conference on Artificial IntelligenceEducational data mining (EDM) is an important research area that implements and develops methods and statistical techniques for discovering new insights in order to improve the performance of the education system. Collecting and analyzing data makes it ...
Feature and Opinion Mining for Customer Review Summarization
PReMI '09: Proceedings of the 3rd International Conference on Pattern Recognition and Machine IntelligenceIn this paper, we present an opinion mining system to identify product features and opinions from review documents. The features and opinions are extracted using semantic and linguistic analysis of text documents. The polarity of opinion sentences is ...
Comments