Skip to main content
SpringerLink
Log in

Sentiment Analysis Techniques and Applications in Education: A Survey

  • Conference paper
  • First Online:
Technology and Innovation in Learning, Teaching and Education (TECH-EDU 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 993))

Abstract

As the interplay between cognition and emotion is involved in every learning process, student profile should be enhanced with information regarding his/her affective state. Sentiment analysis could serve this end, through the analysis of student behavioral traces in teaching-learning environments. The purpose of the present study is to review the status of research on the field of sentiment analysis in the educational domain; exploring different ways in which sentiment analysis has been applied in the educational domain, and analyze the different techniques that researchers have adopted in developing sentiment analysis systems on educational datasets. Five different task types that sentiment analysis has served within the domain were identified, namely: (i) instruction evaluation, (ii) institutional decision/policy making, (iii) intelligent information/learning systems enhancement, (iv) assignment evaluation and feedback improvement, and (v) new research insights. From a technical perspective, a brief explanation of the different sentiment analysis techniques along with representative examples are presented. The character of this work may address the needs of a diverse group of stakeholders, including educators, social sciences researchers as well as researchers, in natural language processing in education.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Picard, R.W., et al.: Affective learning - a Manifesto. BT Technol. J. 22(4), 253–269 (2004)

    Article  Google Scholar 

  2. Illeris, K.: Towards a contemporary and comprehensive theory of learning. Int. J. Lifelong Educ. 22(4), 396–406 (2003)

    Article  Google Scholar 

  3. Park, S.: Building bridge between learning and positive emotion: how to apply emotional factor in instructional designing process? In: Annual Proceedings of Association for Educational Communications and Technology, pp. 679–687 (2004)

    Google Scholar 

  4. D’Mello, S., Graesser, A.: Dynamics of affective states during complex learning. Learn. Instr. 22(2), 145–157 (2012)

    Article  Google Scholar 

  5. Baker, R.S., D’Mello, S.K., Rodrigo, M.T., Graesser, A.C.: Better to be frustrated than bored: the incidence and persistence of affect during interactions with three different computer-based learning environments. Int. J. Hum. Comput. Stud. 68(4), 223–241 (2010)

    Article  Google Scholar 

  6. Hara, N., Kling, R.: Student distress in a web-based distance education course: an ethnographic study of participants’ experiences. Turk. Online J. Distance Educ. 4(2) (2003)

    Google Scholar 

  7. Mohammad, S.M.: 9 – sentiment analysis: detecting valence, emotions, and other affectual states from text. In: Meiselman, H.L. (ed.) Emotion Measurement, pp. 201–237. Woodhead Publishing, Sawston (2016)

    Chapter  Google Scholar 

  8. Feidakis, M., Daradoumis, T., Caballé, S.: Endowing e-learning systems with emotion awareness. In: Proceedings - 3rd IEEE International Conference on Intelligent Networking and Collaborative Systems, pp. 68–75. IEEE (2011)

    Google Scholar 

  9. Alhija, F.N.A., Fresko, B.: Student evaluation of instruction: what can be learned from students’ written comments? Stud. Educ. Eval. 35(1), 37–44 (2009)

    Article  Google Scholar 

  10. Lewis, K.G.: Making sense of student evaluations. New Dir. Teach. Learn. 2001(87), 25–32 (2001)

    Article  Google Scholar 

  11. Koufakou, A., Gosselin, J., Guo, D.: Using data mining to extract knowledge from student evaluation comments in undergraduate courses. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 3138–3142. IEEE (2016)

    Google Scholar 

  12. Dhanalakshmi, V., Bino, D., Saravanan, A.M.: Opinion mining from student feedback data using supervised learning algorithms. In: 3rd MEC International Conference on Big Data and Smart City (ICBDSC). IEEE (2016)

    Google Scholar 

  13. Gottipati, S., Shankararaman, V., Gan, S.: A conceptual framework for analyzing students’ feedback. In: 2017 IEEE Frontiers in Education Conference (FIE), pp. 1–8 (2017)

    Google Scholar 

  14. Azab, M., Mihalcea, R., Abernethy, J.: Analysing ratemyprofessors evaluations across institutions, disciplines, and cultures: the tell-tale signs of a good professor. In: Spiro, E., Ahn, Y. (eds.) Social Informatics, SocInfo 2016. LNCS, vol. 10046, pp. 438–453. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47880-7_27

    Chapter  Google Scholar 

  15. Dalal, R., Safhath, I., Piryani, R., Kappara, D.R., Singh, V.K.: A lexicon pooled machine learning classifier for opinion mining from course feedbacks. In: El-Alfy, E.S., Thampi, S., Takagi, H., Piramuthu, S., Hanne, T. (eds.) Advances in Intelligent Informatics. AISC, vol. 320, pp. 419–428. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-11218-3_38

    Chapter  Google Scholar 

  16. Liu, Z., Liu, S., Liu, L., Sun, J., Peng, X., Wang, T.: Sentiment recognition of online course reviews using multi-swarm optimization-based selected features. Neurocomputing 185, 11–20 (2016)

    Article  Google Scholar 

  17. Colace, F., de Santo, M., Greco, L.: Safe: a sentiment analysis framework for e-learning. Int. J. Emerg. Technol. Learn. 9(6), 37–41 (2014)

    Article  Google Scholar 

  18. Altrabsheh, N., Gaber, M.M., Cocea, M.: SA-E: sentiment analysis for education. Front. Artif. Intell. Appl. 255, 353–362 (2013)

    Google Scholar 

  19. Siemens, G., Long, P.: Penetrating the fog: analytics in learning and education. Educause Rev. 46, 30–32 (2011)

    Google Scholar 

  20. Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., Shum, S.B., Ferguson, R.: Open learning analytics: an integrated & modularized platform, SoLAR (2011)

    Google Scholar 

  21. Janssen, D., Tummel, C., Jeschke, S., Richert, A.: Sentiment analysis of social media for evaluating universities. In: Proceedings of Second International Conference on Digital Information Processing, Data Mining, and Wireless Communications, pp. 49–62 (2015)

    Google Scholar 

  22. Elyasir, A.M.H., Anbananthen, K.S.M.: Opinion mining framework in the education domain. Int. J. Soc. Behav. Educ. Econ. Bus. Ind. Eng. 7(4), 1049–1054 (2013)

    Google Scholar 

  23. Patel, T., Undavia, J., Patel, A.: Sentiment analysis of parents feedback for educational institutes. Int. J. Innov. Emerg. Res. Eng. 2(3), 75–78 (2015)

    Google Scholar 

  24. Kechaou, Z., Ben Ammar, M., Alimi, A.M.: Improving e-learning with sentiment analysis of users’ opinions. In: IEEE Global Engineering Education Conference EDUCON 2011, pp. 1032–1038 (2011)

    Google Scholar 

  25. Scaffidi, C.: Mining online forums for valuable contributions. In: 11th Iberian Conference on Information Systems and Technologies (CISTI) (2016)

    Google Scholar 

  26. Cummins, S., Burd, L., Hatch, A.: Using feedback tags and sentiment analysis to generate sharable learning resources investigating automated sentiment analysis of feedback tags in a programming course. In: 10th IEEE International Conference on Advanced Learning Technologies, pp. 653–657 (2010)

    Google Scholar 

  27. Ortigosa, A., Martín, J.M., Carro, R.M.: Sentiment analysis in Facebook and its application to e-learning. Comput. Human Behav. 31, 527–541 (2014)

    Article  Google Scholar 

  28. Burstein, J., Beigman-Klebanov, B., Nitin, M., Faulkner, A.: Automated sentiment analysis for essay evaluation. In: Shermis, D.M., Burstein, J. (eds.) Handbook of Automated Essay Evaluation: Current Applications and New Directions. Routledge, New York (2013)

    Google Scholar 

  29. Siemens, G., Baker, R.S.J.D.: Learning analytics and educational data mining: towards communication and collaboration. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK 2012, pp. 252–254 (2012)

    Google Scholar 

  30. Wen, M., Yang, D., Rosé, C.: Sentiment analysis in MOOC discussion forums: what does it tell us? In: Proceedings of Educational Data Mining (2014)

    Google Scholar 

  31. Chaplot, D.S., Rhim, E., Kim, J.: Predicting student attrition in MOOCs using sentiment analysis and neural networks. In: 17th International Conference on Artificial Intelligence in Education, AIED-WS 2015, vol. 1432, pp. 7–12 (2015)

    Google Scholar 

  32. Oliveiar, L., Figueira, A.: Visualization of sentiment spread on social networked content: learning analytics for integrated learning environments. In: 2017 IEEE Global Engineering Education Conference (EDUCON), pp. 1290–1298 (2017)

    Google Scholar 

  33. Tucker, C.S., Pursel, B., Divinsky, A.: Mining student-generated textual data in MOOCs and quantifying their effects on student performance and learning outcomes. ASEE Comput. Educ. J. 5(4), 84 (2014)

    Google Scholar 

  34. Liu, B.: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press, Cambridge (2015)

    Book  Google Scholar 

  35. Ahire, S.: A survey of sentiment lexicons (2015)

    Google Scholar 

  36. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of 2004 ACM SIGKDD International Conference on Knowledge Discovery Data Mining - KDD 2004 (2004)

    Google Scholar 

  37. Rani, S., Kumar, P.: A sentiment analysis system to improve teaching and learning. Computer 50(5), 36–43 (2017)

    Article  Google Scholar 

  38. Mohammad, S.M., Turney, P.D.: Crowdsourcing a word–emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)

    Article  MathSciNet  Google Scholar 

  39. Kaewyong, P., Sukprasert, A., Salim, N., Phang, F.A.: The possibility of students’ comments automatic interpret using lexicon based sentiment analysis to teacher evaluation. In: Proceedings of the 3rd International Conference on Artificial Intelligence and Computer Science, AICS 2015 (2015)

    Google Scholar 

  40. Rajput, Q., Haider, S., Ghani, S.: Lexicon-based sentiment analysis of teachers’ evaluation. Appl. Comput. Intell. Soft Comput. 2016, 12 (2016)

    Article  Google Scholar 

  41. Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Lang. Resour. Eval. 39(2–3), 165–210 (2005)

    Article  Google Scholar 

  42. Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)

    Article  Google Scholar 

  43. Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. The MIT Press, London (1999)

    MATH  Google Scholar 

  44. Wang, G., Sun, J., Ma, J., Xu, K., Gu, J.: Sentiment classification: the contribution of ensemble learning. Decis. Support Syst. 57(1), 77–93 (2014)

    Article  Google Scholar 

  45. Mejova, Y., Srinivasan, P.: Exploring feature definition and selection for sentiment classifiers. In: Fifth International AAAI Conference on Weblogs and Social Media, pp. 546–549 (2011)

    Google Scholar 

  46. Nitin, G.I., Swapna, G., Shankararaman, V.: Analyzing educational comments for topics and sentiments: a text analytics approach. In: Proceedings - Frontiers in Education Conference, FIE (2015)

    Google Scholar 

  47. Nasim, Z., Rajput, Q., Haider, S.: Sentiment analysis of student feedback using machine learning and lexicon based approaches. In: 2017 International Conference on Research and Innovation in Information Systems (ICRIIS) (2017)

    Google Scholar 

  48. Kumar, A., Jain, R.: Sentiment analysis and feedback evaluation. In: Proceedings of the 2015 IEEE 3rd International Conference on MOOCs, Innovation and Technology in Education, MITE 2015, pp. 433–436 (2015)

    Google Scholar 

  49. Ullah, M.A.: Sentiment analysis of students feedback: a study towards optimal tools. In: 2016 International Workshop on Computational Intelligence, pp. 175–180 (2016)

    Google Scholar 

  50. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Human Language Technologies Conference/Conference on Empirical Methods in Natural Language Processing, HLT/EMNLP 2005 (2005)

    Google Scholar 

  51. Strapparava, C., Valitutti, A.: WordNet affect: an affective extension of WordNet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation, pp. 1083–1086 (2004)

    Google Scholar 

  52. Stone, P.J., Dunphy, D.C., Smith, M.S.: The General Inquirer: A Computer Approach to Content Analysis. M.I.T Press, Oxford (1966)

    Google Scholar 

  53. El-Halees, A.: Mining feature-opinion in educational data for course improvement. Int. J. New Comput. Archit. Their Appl. 1(4) (2011)

    Google Scholar 

  54. Tian, F., Gao, P., Li, L., Zhang, W., Liang, H., Qian, Y., Zhao, R.: Recognizing and regulating e-learners’ emotions based on interactive Chinese texts in e-learning systems. Knowl.-Based Syst. 55, 148–164 (2014)

    Article  Google Scholar 

  55. Tian, F., Liang, H., Li, L., Zheng, Q.: Sentiment classification in turn-level interactive Chinese texts of e-learning applications. In: 2012 IEEE 12th International Conference on Advanced Learning Technologies, pp. 480–484 (2012)

    Google Scholar 

  56. Dietterich, T.G.: Machine learning research: four current directions. AI Mag. 18, 97–136 (1997)

    Google Scholar 

  57. Polikar, R.: Ensemble based systems in decision making. Circ. Syst. Mag. IEEE 6(3), 21–45 (2006)

    Article  Google Scholar 

  58. Bashri, M.F.A., Kusumaningrum, R.: Sentiment analysis using Latent Dirichlet Allocation and topic polarity wordcloud visualization. In: 2017 5th International Conference on Information and Communication Technology (ICoIC7) (2017)

    Google Scholar 

  59. Bakharia, A.: Towards cross-domain MOOC forum post classification. In: Proceedings of the Third ACM Conference Learning @ Scale - L@S 2016, pp. 253–256 (2016)

    Google Scholar 

  60. Pong-Inwong, C., Kaewmak, K.: Improved sentiment analysis for teaching evaluation using feature selection and voting ensemble learning integration. In: 2016 2nd IEEE International Conference on Computer and Communications (ICCC), pp. 1222–1225 (2016)

    Google Scholar 

  61. Altrabsheh, N., Cocea, M., Fallahkhair, S.: Learning sentiment from students’ feedback for real-time interventions in classrooms. In: Bouchachia, A. (ed.) Adaptive and Intelligent Systems: Third International Conference, ICAIS 2014. LNCS, pp. 40–49. Springer International Publishing, Cham (2014). https://doi.org/10.1007/978-3-319-11298-5_5

    Chapter  Google Scholar 

  62. Altrabsheh, N., Cocea, M., Fallahkhair, S.: Sentiment analysis: towards a tool for analysing real-time students feedback. In: Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, pp. 419–423 (2014)

    Google Scholar 

  63. Valakunde, N.D., Patwardhan, M.S.: Multi-aspect and multi-class based document sentiment analysis of educational data catering accreditation process. In: Proceedings - 2013 International Conference on Cloud and Ubiquitous Computing and Emerging Technologies, CUBE 2013, pp. 188–192 (2013)

    Google Scholar 

  64. Wei, X., Lin, H., Yang, L., Yu, Y.: A convolution-LSTM-based deep neural network for cross-domain MOOC forum post classification. Information 8(3), 92 (2017)

    Article  Google Scholar 

  65. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Pearson Education, New Jersey (2010)

    MATH  Google Scholar 

  66. Zhou, Z.-H.: A brief introduction to weakly supervised learning. Natl. Sci. Rev. 5, 44–53 (2017)

    Article  Google Scholar 

  67. Ramesh, A., Kumar, S.H., Foulds, J., Getoor, L.: Weakly supervised models of aspect-sentiment for online course discussion forums. In: ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference, vol. 1, pp. 74–83 (2015)

    Google Scholar 

  68. Jagarlamudi, J., Daum, H.: Incorporating lexical priors into topic models. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (2012)

    Google Scholar 

  69. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends® Inf. Retr. 2, 1–135 (2008)

    Article  Google Scholar 

  70. Read, J.: Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proceedings of the ACL Student Research Workshop on - ACL 2005 (2005)

    Google Scholar 

  71. Chatterjee, C., Chakma, K.A.: Comparison between sentiment analysis of student feedback at sentence level and at token level. IJCSN Int. J. Comput. Sci. Netw. 4(3), 482–486 (2015)

    Google Scholar 

  72. Sokolova, M.M., Lapalme, G.A.: A systematic analysis of performance measures for classification tasks. Inf. Process Manag. 45(4), 427–437 (2009)

    Article  Google Scholar 

  73. Tripathy, A., Agrawal, A., Rath, S.K.: Classification of sentiment reviews using n-gram machine learning approach. Expert Syst. Appl. 57, 117–126 (2016)

    Article  Google Scholar 

  74. Rushdi Saleh, M., Martín-Valdivia, M.T., Montejo-Ráez, A., Ureña-López, L.A.: Experiments with SVM to classify opinions in different domains. Expert Syst. Appl. 38(12), 14799–14804. https://doi.org/10.1016/j.eswa.2011.05.070

    Article  Google Scholar 

  75. Xia, R., Zong, C., Li, S.: Ensemble of feature sets and classification algorithms for sentiment classification. Inf. Sci. 181(6), 1138–1152 (2011)

    Article  Google Scholar 

  76. Miaomiao, W., Yang, D., Rosé, P.: Sentiment analysis in MOOC discussion forums: what does it tell us? In Stamper, J., Pardos, Z., Mavrikis, M., McLaren, B.M. (eds.) Proceedings of 7th International Conference on Educational Data Mining, pp. 130–137, London (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Early Childhood Education, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Foteini S. Dolianiti

  2. Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Dimitrios Iakovakis

  3. Faculdade de Motricidade Humana, Universidade de Lisboa, Lisbon, Portugal

    Sofia B. Dias & José A. Diniz

  4. Department of Primary Education, Democritus University of Thrace, Alexandroupolis, Greece

    Sofia Hadjileontiadou

  5. Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Leontios Hadjileontiadis

  6. Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE

    Leontios Hadjileontiadis

Authors
  1. Foteini S. Dolianiti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dimitrios Iakovakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sofia B. Dias
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sofia Hadjileontiadou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. José A. Diniz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Leontios Hadjileontiadis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sofia B. Dias .

Editor information

Editors and Affiliations

  1. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Meni Tsitouridou

  2. Universidade de Lisboa, Lisbon, Portugal

    José A. Diniz

  3. University of Ioannina, Ioannina, Greece

    Tassos A. Mikropoulos

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dolianiti, F.S., Iakovakis, D., Dias, S.B., Hadjileontiadou, S., Diniz, J.A., Hadjileontiadis, L. (2019). Sentiment Analysis Techniques and Applications in Education: A Survey. In: Tsitouridou, M., A. Diniz, J., Mikropoulos, T. (eds) Technology and Innovation in Learning, Teaching and Education. TECH-EDU 2018. Communications in Computer and Information Science, vol 993. Springer, Cham. https://doi.org/10.1007/978-3-030-20954-4_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-20954-4_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20953-7

  • Online ISBN: 978-3-030-20954-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation