Emotion Recognition on E-Learning Community to Improve the Learning Outcomes Using Machine Learning Concepts: A Pilot Study

  • A. Jithendran
  • P. Pranav Karthik
  • S. Santhosh
  • J. NarenEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 141)


E-learning community, with its varied interest and expectations on its learning interface, needs more focus when it comes to providing them with the most suitable learning opportunities. This is a challenge considering the fact that there are millions of users belonging to the group. Hence in order to personalize the interface, simple and robust mechanisms must be framed. Humans exhibit different emotions when tied with varied interests. Emotions are thereby used in determining the user’s interests. Machine learning algorithms help in classifying emotions in an individual depending on the data given as input. Emotions are recognized from the speech of a person, textual sentiments given in a social media, facial images of the user, and facial expression captured during a session of an online tutorial on any subject. From emotions recognized from the above sources, a categorization of user’s likes and dislikes on the content can be made.


Emotion recognition Machine learning Image processing Computer vision Multiple kernel learning 


  1. 1.
    Akputu, O.K., Seng, K.P., Lee, Y., Ang, L.-M.: Emotion recognition using multiple kernel learning toward E-learning applications. ACM Trans. Multimedia Comput. Commun. Appl. 14(1), 20 pp. (2018). Article 1Google Scholar
  2. 2.
    Ashwin, T.S., Jose, J., Raghu, G., Reddy, G.R.M.: An E-learning system with multifacial emotion recognition using supervised machine learning (2015)Google Scholar
  3. 3.
    Atkinson, J., Campos, D.: Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers R. Expert Syst. Appl. 47, 35–41 (2016)CrossRefGoogle Scholar
  4. 4.
    Bakchy, S.C., Ferdous, M.J., Sathi, A.H., Ray, K.C., Imran, F., Ali, M.: Facial expression recognition based on support vector machine using Gabor wavelet filter. In: 2017 2nd International Conference on Electrical & Electronic Engineering (ICEEE), December, pp. 1–4 (2017)Google Scholar
  5. 5.
    Bucak, S.S., Member, S., Jin, R., Jain, A.K.: Multiple kernel learning for visual object recognition: a review 36(7), 1354–1369 (2014)Google Scholar
  6. 6.
    Chen, J., Hai, C.: Dynamic texture and geometry features for facial expression recognition in video, pp. 4967–4971 (2015)Google Scholar
  7. 7.
    Fayek, H.M., Lech, M., Cavedon, L.: Evaluating deep learning architectures for speech emotion recognition. Neural Netw. 92, 60–68 (2017)CrossRefGoogle Scholar
  8. 8.
    Hajar, M., Moatassime, A.: Using YouTube comments for text-based emotion recognition. Procedia Comput. Sci. 83(ANT), 292–299 (2016)Google Scholar
  9. 9.
    Kanade, T., Cohn, J.F.: Comprehensive database for facial expression analysis. The Robotics Institute, University of Pittsburgh, pp. 484–490Google Scholar
  10. 10.
    Kumar, V., Kumar, S., Lawrence, S.: Extraction of emotions from multilingual text using intelligent text processing and computational linguistics. J. Comput. Sci. 21, 316–326 (2017)CrossRefGoogle Scholar
  11. 11.
    Kundu, T.: Advancements and recent trends in emotion recognition using facial image analysis and machine learning models, pp. 1–6 (2017)Google Scholar
  12. 12.
    Lee, S.H., Baddar, W.J., Man, Y.: Collaborative expression representation using peak expression and intra class variation face images for practical subject-independent emotion recognition in videos. Pattern Recognit. 54, 52–67 (2016)CrossRefGoogle Scholar
  13. 13.
    Li, J., Zhang, D., Zhang, J., Zhang, J.: Facial expression recognition with faster R-CNN. Procedia Comput. Sci. 107(ICICT), 135–140 (2017)Google Scholar
  14. 14.
    Litman, D.J., Forbes-Riley, K.: Recognizing student emotions and attitudes on the basis of utterances in spoken tutoring dialogues with both human and computer tutors 48, 559–590 (2006)Google Scholar
  15. 15.
    Mahboob, T., Irfan, S., Karamat, A.: A machine learning approach for student assessment in E-learning using Quinlan’s C4.5, Naïve Bayes and random forest algorithmsGoogle Scholar
  16. 16.
    Mayya, V., Pai, R.M., Pai, M.M.M.: Automatic facial expression recognition using DCNN. Procedia Comput. Sci. 93, pp. 453–461 (2016)Google Scholar
  17. 17.
    Oryina, A.K., Adedolapo, A.O.: Emotion recognition for user centred E-learning. In: 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), Atlanta, GA, pp. 509–514 (2016)Google Scholar
  18. 18.
    Perikos, I., Hatzilygeroudis, I.: Recognizing emotions in text using ensemble of classifiers. Eng. Appl. Artif. Intell. 51, 191–201 (2016)CrossRefGoogle Scholar
  19. 19.
    Poria, S., Cambria, E., Howard, N., Huang, G., Hussain, A.: Neurocomputing fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing 174, 50–59 (2016)CrossRefGoogle Scholar
  20. 20.
    Poria, S., Chaturvedi, I., Cambria, E.: Convolutional MKL based multimodal emotion recognition and sentiment analysis (2016)Google Scholar
  21. 21.
    Taheri, S., Patel, V.M., Chellappa, R.: Component-based recognition of faces and facial expressions 4(4), 360–371 (2013)Google Scholar
  22. 22.
    Tarnowski, P., et al.: Emotion recognition using facial expressions. Procedia Comput. Sci. 108, 1175–1184 (2017)CrossRefGoogle Scholar
  23. 23.
    Wang, X., Nie, D., Lu, B.: Emotional state classification from EEG data using machine learning approach. Neurocomputing 129, 94–106 (2014)CrossRefGoogle Scholar
  24. 24.
    Yan, H.: Collaborative discriminative multi-metric learning for facial expression recognition in video. Pattern Recognit. 75, 33–40 (2018)CrossRefGoogle Scholar
  25. 25.
    Yin, Z., Zhao, M., Wang, Y., Yang, J., Zhang, J.: Recognition of emotions using multimodal physiological signals and an ensemble deep learning model. Comput. Methods Programs Biomed. 140, 93–110 (2017)Google Scholar
  26. 26.
    Zhang, B., Liu, G., Xie, G.: Facial expression recognition using LBP and LPQ based on Gabor wavelet transform (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • A. Jithendran
    • 1
  • P. Pranav Karthik
    • 1
  • S. Santhosh
    • 1
  • J. Naren
    • 1
    Email author
  1. 1.Department of Computer Science and EngineeringSASTRA Deemed to Be UniversityThanjavurIndia

Personalised recommendations