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Sentiment Analysis to Track Emotion and Polarity in Student Fora

Authors:

Andreas F. Gkontzis,

Christoforos V. Karachristos,

Chris T. Panagiotakopoulos,

Elias C. Stavropoulos,

Vassilios S. VerykiosAuthors Info & Claims

PCI '17: Proceedings of the 21st Pan-Hellenic Conference on Informatics

Article No.: 39, Pages 1 - 6

https://doi.org/10.1145/3139367.3139389

Published: 28 September 2017 Publication History

Abstract

The purpose of this paper is to propose a data mining methodology for analysing data relating to the participation of students in the online forum of a postgraduate course at the Hellenic Open University. Data is migrated to MongoDB, a NoSQL database management system, and analysed using the rmongodb package of R statistical environment. We focus in sentiment analysis to extract the emotional knowledge of students' fora. Polarity and emotion are identified in messages and are classified as positive, negative or neutral. Messages are categorized and visualized in six basic emotions, as a multiclass approach in understanding students' written opinion. By identifying sentiment behaviour from students' discussion fora, we are able to assess the effectiveness of the learning environment to improve students' learning experience, tutors' instructional experience and the university's institutional strategic view.

References

[1]

Enrique García, and Cristóbal Romero. 2011. A collaborative educational association rule mining tool. Internet and Higher Education, 14, 77--78.

[2]

Cristóbal Romero and Sebastián Ventura. 2010. Educational Data Mining: A Review of the State of the Art. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 40 (Dec 2010), 601--618.

Digital Library

[3]

Hanna, Margo. 2004. Data mining in the e-learning domain. Computers & Education Journal, 42(3), 267--287.

Digital Library

[4]

Myriam Munezero, Calkin SueroMontero, Maxim Mozgovoy, Erkki Sutinen. 2013. Exploiting sentiment analysis to track emotions in students' learning diaries. In: Koli Calling International Conference on Computing Education Research, 13, 145--152.

Digital Library

[5]

Gauri Shirolkar, Rajiv Shukla, Harsh Shah, Reenal Shah. 2013 Mental State Classification for Hypnotherapy Using Sentiment Analysis. International Journal of Advanced Research in Computer Science and Software Engineering, 3(10), 926--929.

[6]

Lorenzo Gatti and Marco Guerini. 2012. Assessing sentiment strength in words prior polarities. In Proceedings of the 24th International Conference on Computational Linguistics (COLING '12), 361--370, Mumbai, India.

[7]

Daniel Jurafsky and James H. Martin. 2016. Speech and Language Processing, (2nd Ed.), Chapter 6. Naive Bayes and Sentiment Classification.

Digital Library

[8]

Nabeela Altrabsheh, Mohamed Medhat Gaber and Mihaela Cocea. 2013. SA-E: Sentiment analysis for education. Intelligent Decision technologies: Proceedings of the 5th KES International Conference on Intelligent Decision Technologies, 255. 353--362.

[9]

Han Hu, Yonggang Wen, Tat-Seng Chua, and Xuelong Li. 2014. Toward Scalable Systems for Big Data Analytics: A Technology Tutorial. IEEE Access, 2, 652--687.

[10]

Ioannis Konstantinou, Evangelos Angelou, Christina Boumpouka, Dimitrios Tsoumakos, and Nectarios Koziris. 2011. On the elasticity of NoSQL databases over cloud management platforms. In Proceedings of the 20th ACM international conference on Information and knowledge management (CIKM '11), Bettina Berendt, Arjen de Vries, Wenfei Fan, Craig Macdonald, Iadh Ounis, and Ian Ruthven (Eds.). ACM, New York, NY, USA, 2385--2388.

Digital Library

[11]

Cristóbal Romero, Sebastián Ventura, Enrique García. 2008. Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51, 368--384.

Digital Library

[12]

Monika Goyal and Rajan Vohra. 2012. Applications of Data Mining in Higher Education. International Journal of Computer Science Issues, 9 (2), 113--120.

[13]

Vasileios Kagklis, Antonis Lionarakis, Elias. C. Stavropoulos, and Vassilios S. Verykios. 2016. A Learning Analytics Methodology for Student Performance Assessment in a Distance and Open Education Environment. In Proceedings of The Online, Open and Flexible Higher Education Conference on Opportunities and Impact of New Modes of Teaching (EADTU conference 2016), 735--748.

[14]

Vasileios Kagklis, Antonis Lionarakis, Gerasimos Marketos, Chris T. Panagiotakopoulos, Elias C. Stavropoulos, and Vassilios S. Verykios, 2017. Student Admission Data Analytics for Open and Distance Education in Greece. Open Education - The Journal for Open and Distance Education and Educational Technology, to appear.

[15]

Cristóbal Romero, Manuel-Ignacio López, Jose-Maria Luna, and Sebastián Ventura, 2013. Predicting students' final performance from participation in on-line discussion forums. Computers & Education, 68, 458--472.

[16]

Beth Dietz-Uhler and Janet E. Hurn. 2013. Using Learning Analytics to Predict (and Improve) Student Success: A Faculty Perspective. Journal of Interactive Online Learning, 12 (1), 17--26.

[17]

Yvette Awuor and Robert Oboko. 2012. Automatic Assessment of Online Discussions Using Text Mining. International Journal of Machine Learning & Applications, 1(1) 1--7.

[18]

M'hammed Abdous, Wu He, and Cherng-Jyh Yen. 2012. Using Data Mining for Predicting Relationships between Online Question Theme and Final Grade. Educational Technology & Society, 15 (3), 77--88.

[19]

Miaomiao Wen, Diyi Yang, and Carolyn Penstein Rosé, 2014. Sentiment Analysis in MOOC Discussion Forums: What does it tell us? In Proceedings of the 7th International Conference on Educational Data Mining. London, UK.

[20]

Norma Ming, and Eric Baumer. 2011. Using Text Mining to Characterize Online Discussion Facilitation. Journal of Asynchronous Learning Networks, 15 (2), 71--109.

[21]

Diyi Yang, Mario Piergallini, Iris Howley, and Carolyn Rose. 2014. Forum thread recommendation for massive open online courses. In Proceedings of the 7th International Conf. on Educational Data Mining. London, 257--260.

[22]

Myriam Munezero, Calkin Suero Montero, Maxim Mozgovoy, and Erkki Sutinen. 2013. Exploiting Sentiment Analysis to Track Emotions in Students' Learning Diaries. In Proceedings of the Koli Calling International Conference on Computing Education Research, ACM Press, New York, NY, 13, 145--152.

Digital Library

[23]

Conrad S. Tucker, Barton Pursel and Anna Divinsky. 2014. Mining Student-Generated Textual Data in MOOCS and Quantifying Their Effects on Student Performance and Learning Outcomes, ASEE Computers in Education Journal (CoEd), 5 (4).

[24]

Michalis Xenos, Christos Pierrakeas, and Panagiotis Pintelas. 2002. A survey on student dropout rates and dropout causes concerning the students in the course of informatics of the Hellenic Open University. Computers & Education, 39(4), 361--377.

[25]

Dionysis Karaiskakis, Dimitrios Kalles, and Thanassis Hadzilacos. 2008. Profiling Group Activity of Online Academic Workspaces: the Hellenic Open University case study. International Journal of Web-based Learning and Teaching Technology, 3(3), 1--15.

[26]

Sotiris Kotsiantis, Christos Pierrakeas, and Panagiotis Pintelas. (2004). Predicting students' performance in distance learning using machine learning techniques. Applied Artificial Intelligence, 18(5), 411--426.

[27]

Elvira Lotsari, Vassilios S. Verykios, Chris T. Panagiotakopoulos, and Dimitris Kalles. 2014. A Learning Analytics Methodology for Student Profiling. In Proceedings of the 8th. Hellenic Conference on Artificial Intelligence. Greece, 300--312.

[28]

Vasileios Kagklis, Anthi Karatrantou, Maria Tantoula, Chris T. Panagiotakopoulos, and Vassilios S. Verykios. 2015. A learning analytics methodology for detecting sentiment in student fora: A Case Study in Distance Education. European Journal of Open, Distance and E-learning, 18(2), 74--94.

[29]

Lamprini Rori, Pantelis Agathangelou, Ioannis Katakis, Dimitrios Gunopulos and Barry Richards. 2016. Online Dynamics of Far-Right and Far-Left Communities: The Case of the September 2015 Parliamentary Elections in Greece. Presented at the European Consortium for Political Research (ECPR) Joint Sessions, Scuola Normale Superiore, Scuola Superiore Sant' Anna and University of Pisa, Pisa, 24--28.

[30]

ABM Moniruzzaman and Syed Hossain. 2013. NoSQL Database: New era of Databases for Big Data Analytics Classification, Characteristics and Comparison. International Journal of Database Theory and Application, 6(4), 1--13.

[31]

Eelco Plugge, DUPTim Hawkins and Peter Mermbrey. 2010. The Definitive Guide to MongoDB: The NoSQL Database for Cloud and Desktop Computing. 1st Apress, Berkely.

Digital Library

[32]

Kyle Banker, 2012. MongoDB in Action. Manning Publications Co, Shelter Island, NY.

Digital Library

[33]

Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), 347--354.

Digital Library

[34]

Carlo Strapparava and Alessandro Valitutti. 2004. Wordnet-affect: an affective extension of wordnet. In Proceedings of the 4th International Conference on Language Resources and Evaluation, Lisbon, Portugal, 1083--1086.

Cited By

Vorvilas GPantazi DPaxinou EFeretzakis GKalles DKameas AKarousos NVerykios V(2024)An Automated Text Summarization Approach for Open-ended Responses in Student Online Surveys2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA)10.1109/IISA62523.2024.10786702(1-7)Online publication date: 17-Jul-2024
https://doi.org/10.1109/IISA62523.2024.10786702
Lin YNixon N(2024)Transitioning to Online Instructions and COVID-19 Response: A View from Mining Emergent College Students Discourse in Online Discussion ForumInternational Journal of Artificial Intelligence in Education10.1007/s40593-024-00411-334:3(706-731)Online publication date: 25-Jun-2024
https://doi.org/10.1007/s40593-024-00411-3
Nair RStaubitz TMeinel C(2023)The Influence of Teacher Interactions on Sentiment Development in MOOC Discussion Forums2023 IEEE Learning with MOOCS (LWMOOCS)10.1109/LWMOOCS58322.2023.10306198(1-6)Online publication date: 11-Oct-2023
https://doi.org/10.1109/LWMOOCS58322.2023.10306198
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Index Terms

Sentiment Analysis to Track Emotion and Polarity in Student Fora
1. Applied computing
  1. Education
    1. E-learning
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics

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cover image ACM Other conferences

PCI '17: Proceedings of the 21st Pan-Hellenic Conference on Informatics

September 2017

322 pages

ISBN:9781450353557

DOI:10.1145/3139367

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Greek Com Soc: Greek Computer Society
University of Thessaly: University of Thessaly, Volos, Greece

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 September 2017

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PCI 2017

PCI 2017: 21st PAN-HELLENIC CONFERENCE ON INFORMATICS

September 28 - 30, 2017

Larissa, Greece

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Overall Acceptance Rate 190 of 390 submissions, 49%

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Cited By

Vorvilas GPantazi DPaxinou EFeretzakis GKalles DKameas AKarousos NVerykios V(2024)An Automated Text Summarization Approach for Open-ended Responses in Student Online Surveys2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA)10.1109/IISA62523.2024.10786702(1-7)Online publication date: 17-Jul-2024
https://doi.org/10.1109/IISA62523.2024.10786702
Lin YNixon N(2024)Transitioning to Online Instructions and COVID-19 Response: A View from Mining Emergent College Students Discourse in Online Discussion ForumInternational Journal of Artificial Intelligence in Education10.1007/s40593-024-00411-334:3(706-731)Online publication date: 25-Jun-2024
https://doi.org/10.1007/s40593-024-00411-3
Nair RStaubitz TMeinel C(2023)The Influence of Teacher Interactions on Sentiment Development in MOOC Discussion Forums2023 IEEE Learning with MOOCS (LWMOOCS)10.1109/LWMOOCS58322.2023.10306198(1-6)Online publication date: 11-Oct-2023
https://doi.org/10.1109/LWMOOCS58322.2023.10306198
Bittencourt IChalco GSantos JFernandes SSilva JBatista NHutz CIsotani S(2023)Positive Artificial Intelligence in Education (P-AIED): A RoadmapInternational Journal of Artificial Intelligence in Education10.1007/s40593-023-00357-y34:3(732-792)Online publication date: 3-Aug-2023
https://doi.org/10.1007/s40593-023-00357-y
Scianna JKaliisa R(2023)‘SSEEN’: a networked approach to uncover connections between sentiment, social, and epistemic elements of student online forum discourseEducational technology research and development10.1007/s11423-023-10310-472:5(2817-2839)Online publication date: 6-Nov-2023
https://doi.org/10.1007/s11423-023-10310-4
Obaidi MNagel LSpecht AKlünder J(2022)Sentiment analysis tools in software engineeringInformation and Software Technology10.1016/j.infsof.2022.107018151:COnline publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.infsof.2022.107018
Obaidi MKlünder J(2021)Development and Application of Sentiment Analysis Tools in Software Engineering: A Systematic Literature ReviewProceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering10.1145/3463274.3463328(80-89)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3463274.3463328
Sharma CSaxena P(2021)Stress Analysis for Students in Online Classes2021 Grace Hopper Celebration India (GHCI)10.1109/GHCI50508.2021.9514059(1-5)Online publication date: 19-Feb-2021
https://doi.org/10.1109/GHCI50508.2021.9514059
Gkontzis AKotsiantis SKalles DPanagiotakopoulos CVerykios V(2020)Polarity, emotions and online activity of students and tutors as features in predicting gradesIntelligent Decision Technologies10.3233/IDT-19013714:3(409-436)Online publication date: 29-Sep-2020
https://doi.org/10.3233/IDT-190137
Wook MVasanthan SRamli SRazali NHasbullah NZainudin N(2020)Exploring Students’ Feedback in Online Assessment System Using Opinion Mining TechniqueInternational Journal of Information and Education Technology10.18178/ijiet.2020.10.9.144010:9(664-668)Online publication date: 2020
https://doi.org/10.18178/ijiet.2020.10.9.1440
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