The high pace rising global competitions across education sector has forced institutions to enhance aforesaid aspects, which require assessing students or related stakeholders’ perception and opinion towards the learning materials, courses, learning methods or pedagogies, etc. To achieve it, the use of reviews by students can of paramount significance; yet, annotating student’s opinion over huge heterogenous and unstructured data remains a tedious task. Though, the artificial intelligence (AI) and natural language processing (NLP) techniques can play decisive role; yet the conventional unsupervised lexicon, corpus-based solutions, and machine learning and/or deep driven approaches are found limited due to the different issues like class-imbalance, lack of contextual details, lack of long-term dependency, convergence, local minima etc. The aforesaid challenges can be severe over large inputs in Big Data ecosystems. In this reference, this paper proposed an outlier resilient semantic featuring deep driven sentiment analysis model (ORDSAENet) for educational domain sentiment annotations. To address data heterogeneity and unstructured-ness over unpredictable digital media, the ORDSAENet applies varied pre-processing methods including missing value removal, Unicode normalization, Emoji and Website link removal, removal of the words with numeric values, punctuations removal, lower case conversion, stop-word removal, lemmatization, and tokenization. Moreover, it applies a text size-constrained criteria to remove outlier texts from the input and hence improve ROI-specific learning for accurate annotation. The tokenized data was processed for Word2Vec assisted continuous bag-of-words (CBOW) semantic embedding followed by synthetic minority over-sampling with edited nearest neighbor (SMOTE-ENN) resampling. The resampled embedding matrix was then processed for Bi-LSTM feature extraction and learning that retains both local as well as contextual features to achieve efficient learning and classification. Executing ORDSAENet model over educational review dataset encompassing both qualitative reviews as well as quantitative ratings for the online courses, revealed that the proposed approach achieves average sentiment annotation accuracy, precision, recall, and F-Measure of 95.87%, 95.26%, 95.06% and 95.15%, respectively, which is higher than the LSTM driven standalone feature learning solutions and other state-of-arts. The overall simulation results and allied inferences confirm robustness of the ORDSAENet model towards real-time educational sentiment annotation solution.
M. Bansal, S. Verma, K. Vig, and K. Kakran, “Opinion Mining from Student Feedback Data Using Supervised Learning Algorithms,” Lecture
Notes in Networks and Systems, pp. 411–418, 2022, doi: 10.1007/978-3-031-12413-6_32.
Ligthart, C. Catal, and B. Tekinerdogan, “Systematic reviews in sentiment analysis: a tertiary study,” Artificial Intelligence Review, vol. 54, no. 7,
pp. 4997–5053, Mar. 2021, doi: 10.1007/s10462-021-09973-3.
E. Cambria, “Affective Computing and Sentiment Analysis,” IEEE Intelligent Systems, vol. 31, no. 2, pp. 102–107, Mar. 2016, doi:
10.1109/mis.2016.31.
B. Liu, “Sentiment Analysis and Opinion Mining,” Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1–167, May 2012, doi:
10.2200/s00416ed1v01y201204hlt016.
X. Tao, Y. Li, and R. Nayak, “A knowledge retrieval model using ontology mining and user profiling,” Integrated Computer-Aided Engineering,
vol. 15, no. 4, pp. 313–329, Jul. 2008, doi: 10.3233/ica-2008-15404.
X. Tao, T. B. Shaik, N. Higgins, R. Gururajan, and X. Zhou, “Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology
and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities,” Sensors, vol. 21, no. 3, p. 776, Jan. 2021, doi:
10.3390/s21030776.
X. Tao, X. Zhou, J. Zhang, and J. Yong, “Sentiment Analysis for Depression Detection on Social Networks,” Lecture Notes in Computer Science,
pp. 807–810, 2016, doi: 10.1007/978-3-319-49586-6_59.
X. Zhou, X. Tao, J. Yong, and Z. Yang, “Sentiment analysis on tweets for social events,” Proceedings of the 2013 IEEE 17th International
Conference on Computer Supported Cooperative Work in Design (CSCWD), Jun. 2013, doi: 10.1109/cscwd.2013.6581022.
T. Shaik et al., “FedStack: Personalized activity monitoring using stacked federated learning,” Knowledge-Based Systems, vol. 257, p. 109929,
Dec. 2022, doi: 10.1016/j.knosys.2022.109929.
T. Shaik et al., “A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis,”
IEEE Access, vol. 10, pp. 56720–56739, 2022, doi: 10.1109/access.2022.3177752.
T. Shaik, X. Tao, C. Dann, H. Xie, Y. Li, and L. Galligan, “Sentiment analysis and opinion mining on educational data: A survey,” Natural Language
Processing Journal, vol. 2, p. 100003, Mar. 2023, doi: 10.1016/j.nlp.2022.100003.
V. K. Yeruva, M. Chandrashekar, Y. Lee, J. Rydberg-Cox, V. Blanton, and N. A. Oyler, “Interpretation of Sentiment Analysis with Human-in-the-
Loop,” 2020 IEEE International Conference on Big Data (Big Data), Dec. 2020, doi: 10.1109/bigdata50022.2020.9378221.
M. Elfeky, T. S. Y. Masadeh, and M. Y. H. Elbyaly, “Advance organizers in flipped classroom via e-learning management system and the promotion
of integrated science process skills,” Thinking Skills and Creativity, vol. 35, p. 100622, Mar. 2020, doi: 10.1016/j.tsc.2019.100622.
L. McKinney, A. B. Burridge, M. M. Lee, G. V. Bourdeau, and M. Miller-Waters, “Incentivizing Full-Time Enrollment at Community Colleges:
What Influences Students’ Decision to Take More Courses?,” Community College Review, vol. 50, no. 2, pp. 144–170, Feb. 2022, doi:
10.1177/00915521211061416.
H. Zhao, Z. Liu, X. Yao, and Q. Yang, “A machine learning-based sentiment analysis of online product reviews with a novel term weighting and
feature selection approach,” Information Processing & Management, vol. 58, no. 5, p. 102656, Sep. 2021, doi: 10.1016/j.ipm.2021.102656.
Y. Zhang, J. Wang, and X. Zhang, “Conciseness is better: Recurrent attention LSTM model for document-level sentiment analysis,”
Neurocomputing, vol. 462, pp. 101–112, Oct. 2021, doi: 10.1016/j.neucom.2021.07.072.
Z. Liu, C. Yang, S. Rüdian, S. Liu, L. Zhao, and T. Wang, “Temporal emotion-aspect modeling for discovering what students are concerned about
in online course forums,” Interactive Learning Environments, vol. 27, no. 5–6, pp. 598–627, Apr. 2019, doi: 10.1080/10494820.2019.1610449.
J. J. Zhu, Y.-C. Chang, C.-H. Ku, S. Y. Li, and C.-J. Chen, “Online critical review classification in response strategy and service provider rating:
Algorithms from heuristic processing, sentiment analysis to deep learning,” Journal of Business Research, vol. 129, pp. 860–877, May 2021, doi:
10.1016/j.jbusres.2020.11.007.
F. A. Acheampong, H. Nunoo-Mensah, and W. Chen, “Transformer models for text-based emotion detection: a review of BERT-based approaches,”
Artificial Intelligence Review, vol. 54, no. 8, pp. 5789–5829, Feb. 2021, doi: 10.1007/s10462-021-09958-2.
V. Kuleto et al., “Exploring Opportunities and Challenges of Artificial Intelligence and Machine Learning in Higher Education Institutions,”
Sustainability, vol. 13, no. 18, p. 10424, Sep. 2021, doi: 10.3390/su131810424.
F. S. Dolianiti, D. Iakovakis, S. B. Dias, S. Hadjileontiadou, J. A. Diniz, and L. Hadjileontiadis, “Sentiment Analysis Techniques and Applications
in Education: A Survey,” Technology and Innovation in Learning, Teaching and Education, pp. 412–427, 2019, doi: 10.1007/978-3-030-20954-
4_31.
R. Catelli, S. Pelosi, and M. Esposito, “Lexicon-Based vs. Bert-Based Sentiment Analysis: A Comparative Study in Italian,” Electronics, vol. 11,
no. 3, p. 374, Jan. 2022, doi: 10.3390/electronics11030374.
V. Hatzivassiloglou and K. R. McKeown, “Predicting the semantic orientation of adjectives,” Proceedings of the 35th annual meeting on Association
for Computational Linguistics -, 1997, doi: 10.3115/976909.979640.
Taboada, M., Gillies, M.A., McFetridge, Sentiment classification techniques for tracking literary reputation. In: LREC Work shop: To-Wards
Computational Models of Literary Analysis. Citeseer, pp. 36–43. P., 2006.
Benamara, F., Cesarano, C., Picariello, A., Recupero, D.R., Subrahmanian, V.S., Sentiment analysis: Adjectives and adverbs are better than
adjectives alone. ICWSM 7, 203–206.
Vermeij, M., The orientation of user opinions through adverbs, verbs and nouns. In: 3rd Twente Student Conference on IT, Enschede June. Citeseer.
2005.
Neviarouskaya, H. Prendinger, and M. Ishizuka, “Compositionality Principle in Recognition of Fine-Grained Emotions from Text,” Proceedings of
the International AAAI Conference on Web and Social Media, vol. 3, no. 1, pp. 278–281, Mar. 2009, doi: 10.1609/icwsm.v3i1.13987.
Baccianella, S., Esuli, A., Sebastiani, F., Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining, 2010.
B. Maegaard, K. Choukri, N. Calzolari, and J. Odijk, “ELRA – European Language Resources Association-Background, Recent Developments and
Future Perspectives,” Language Resources and Evaluation, vol. 39, no. 1, pp. 9–23, Feb. 2005, doi: 10.1007/s10579-005-2692-5.
T. Wilson et al., “OpinionFinder,” Proceedings of HLT/EMNLP on Interactive Demonstrations -, 2005, doi: 10.3115/1225733.1225751.
D. Boullier and A. Lohard, “Opinion mining et Sentiment analysis : résumé,” Opinion mining et Sentiment analysis, 2012, doi:
10.4000/books.oep.223.
T. Wilson, J. Wiebe, and P. Hoffmann, “Recognizing contextual polarity in phrase-level sentiment analysis,” Proceedings of the conference on
Human Language Technology and Empirical Methods in Natural Language Processing - HLT ’05, 2005, doi: 10.3115/1220575.1220619.
Nielsen, F.Å, A new anew: Evaluation of a word list for sentiment analysis in microblogs. arXiv preprint arXiv:1103.2903, 2011.
Neviarouskaya, H. Prendinger, and M. Ishizuka, “SentiFul: Generating a reliable lexicon for sentiment analysis,” 2009 3rd International Conference
on Affective Computing and Intelligent Interaction and Workshops, Sep. 2009, doi: 10.1109/acii.2009.5349575.
Hutto and E. Gilbert, “VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text,” Proceedings of the International
AAAI Conference on Web and Social Media, vol. 8, no. 1, pp. 216–225, May 2014, doi: 10.1609/icwsm.v8i1.14550.
Tzacheva and A. Easwaran, “Emotion Detection and Opinion Mining from Student Comments for Teaching Innovation Assessment,” International
Journal of Education (IJE), vol. 09, no. 02, pp. 21–32, Jun. 2021, doi: 10.5121/ije2021.9203.
Preethi P, Yogapriya J, Nivetha L and R.Madanachitran, “Transfer Driven Ensemble Learning Approach using ROI Pooling CNN For Enhanced
Breast Cancer Diagnosis, Journal of Machine and Computing, vol.3, no.3, pp. 297-311, July 2023. doi: 10.53759/7669/jmc202303026.
Ortony, A., Turner, T.J.,. What’s basic about basic emotions? Psychol. Rev. 97 (3), 315, 1990.
X. Zhu, S. Kiritchenko, and S. Mohammad, “NRC-Canada-2014: Recent Improvements in the Sentiment Analysis of Tweets,” Proceedings of the
8th International Workshop on Semantic Evaluation (SemEval 2014), 2014, doi: 10.3115/v1/s14-2077.
M. L. Maher, C. Latulipe, H. Lipford, and A. Rorrer, “Flipped Classroom Strategies for CS Education,” Proceedings of the 46th ACM Technical
Symposium on Computer Science Education, Feb. 2015, doi: 10.1145/2676723.2677252.
C. Latulipe, N. B. Long, and C. E. Seminario, “Structuring Flipped Classes with Lightweight Teams and Gamification,” Proceedings of the 46th
ACM Technical Symposium on Computer Science Education, Feb. 2015, doi: 10.1145/2676723.2677240.
S. MacNeil, C. Latulipe, B. Long, and A. Yadav, “Exploring Lightweight Teams in a Distributed Learning Environment,” Proceedings of the 47th
ACM Technical Symposium on Computing Science Education, Feb. 2016, doi: 10.1145/2839509.2844577.
J. Melba Rosalind and S. Suguna, “Predicting Students’ Satisfaction Towards Online Courses Using Aspect-Based Sentiment Analysis,” IFIP
Advances in Information and Communication Technology, pp. 20–35, 2022, doi: 10.1007/978-3-031-11633-9_3.
F. Alqasemi, A. Abdelwahab, and H. Abdelkader, “Constructing automatic domain-specific sentiment lexicon using KNN search via terms
discrimination vectors,” International Journal of Computers and Applications, vol. 41, no. 2, pp. 129–139, Dec. 2017, doi:
10.1080/1206212x.2017.1409477.
U. Ö. Osmanoğlu, O. N. Atak, K. Çağlar, H. Kayhan, And T. Can, “Sentiment Analysis for Distance Education Course Materials: A Machine
Learning Approach,” Journal of Educational Technology and Online Learning, vol. 3, no. 1, pp. 31–48, Jan. 2020, doi: 10.31681/jetol.663733.
H. H. Lwin, S. Oo, K. Z. Ye, K. Kyaw Lin, W. P. Aung, and P. Paing Ko, “Feedback Analysis in Outcome Base Education Using Machine Learning,”
2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-
CON), Jun. 2020, doi: 10.1109/ecti-con49241.2020.9158328.
R. Faizi, “A Sentiment-Based Approach to Predict Learners’ Perceptions Towards YouTube Educational Videos,” Lecture Notes in Networks and
Systems, pp. 549–556, 2022, doi: 10.1007/978-3-030-96299-9_52.
Kaur, C., Boush, M.S.A., Hassen, S.M., Hakami, W.A., Ab-dalraheem, M.H.O., Galam, N.M., Hadi, N.A., Hadi, N.A., Benjeed, A.O.S., 2022.
Incorporating sentimental analysis into development of a hybrid classification model. Int. J. Health Sci. 6 (S1), 1709–1720.
F. S. Dolianiti, D. Iakovakis, S. B. Dias, S. Hadjileontiadou, J. A. Diniz, and L. Hadjileontiadis, “Sentiment Analysis Techniques and Applications
in Education: A Survey,” Technology and Innovation in Learning, Teaching and Education, pp. 412–427, 2019, doi: 10.1007/978-3-030-20954-
4_31.
M. Ahmad, S. Aftab, M. Salman, and N. Hameed, “Sentiment Analysis using SVM: A Systematic Literature Review,” International Journal of
Advanced Computer Science and Applications, vol. 9, no. 2, 2018, doi: 10.14569/ijacsa.2018.090226.
M. Sivakumar and U. S. Reddy, “Aspect based sentiment analysis of students opinion using machine learning techniques,” 2017 International
Conference on Inventive Computing and Informatics (ICICI), Nov. 2017, doi: 10.1109/icici.2017.8365231.
N. Nikolić, O. Grljević, and A. Kovačević, “Aspect-based sentiment analysis of reviews in the domain of higher education,” The Electronic Library,
vol. 38, no. 1, pp. 44–64, Feb. 2020, doi: 10.1108/el-06-2019-0140.
Yang, R., Machine learning and deep learning for sentiment analysis over students’ reviews: An overview study, 2021.
J. Ding, H. Sun, X. Wang, and X. Liu, “Entity-level sentiment analysis of issue comments,” Proceedings of the 3rd International Workshop on
Emotion Awareness in Software Engineering, Jun. 2018, doi: 10.1145/3194932.3194935.
H. Li and W. Lu, “Learning Latent Sentiment Scopes for Entity-Level Sentiment Analysis,” Proceedings of the AAAI Conference on Artificial
Intelligence, vol. 31, no. 1, Feb. 2017, doi: 10.1609/aaai.v31i1.11016.
Kalaiselvi Balaraman and S.P.Angelin Claret, “Hybrid Resnet and Bidirectional LSTM-Based Deep Learning Model for Cardiovascular Disease
Detection Using PPG Signals, Journal of Machine and Computing, vol.3, no.3, pp. 351-359, July 2023. doi: 10.53759/7669/jmc202303030.
M. Edalati, A. S. Imran, Z. Kastrati, and S. M. Daudpota, “The Potential of Machine Learning Algorithms for Sentiment Classification of Students’
Feedback on MOOC,” Intelligent Systems and Applications, pp. 11–22, Aug. 2021, doi: 10.1007/978-3-030-82199-9_2.
D. Wehbe, A. Alhammadi, H. Almaskari, K. Alsereidi, and H. Ismail, “UAE e-Learning Sentiment Analysis Framework,” ArabWIC 2021: The 7th
Annual International Conference on Arab Women in Computing in Conjunction with the 2nd Forum of Women in Research, Sharjah, UAE, Aug.
2021, doi: 10.1145/3485557.3485570.
Deepa Devasenapathy, Vidhya K, Anna Alphy, Finney Daniel Shadrach, Kathirvelu M and Jayaraj Velusamy, “Kidney Impairment Prediction Due
to Diabetes Using Extended Ensemble Learning Machine Algorithm, Journal of Machine and Computing, vol.3, no.3, pp. 312-325, July 2023. doi:
10.53759/7669/jmc202303027.
N. Dehbozorgi and D. P. Mohandoss, “Aspect-Based Emotion Analysis on Speech for Predicting Performance in Collaborative Learning,” 2021
IEEE Frontiers in Education Conference (FIE), Oct. 2021, doi: 10.1109/fie49875.2021.9637330.
Z. Kastrati, A. S. Imran, and A. Kurti, “Weakly Supervised Framework for Aspect-Based Sentiment Analysis on Students’ Reviews of MOOCs,”
IEEE Access, vol. 8, pp. 106799–106810, 2020, doi: 10.1109/access.2020.3000739.
L. C. Yu et al., “Improving early prediction of academic failure using sentiment analysis on self-evaluated comments,” Journal of Computer Assisted
Learning, vol. 34, no. 4, pp. 358–365, Mar. 2018, doi: 10.1111/jcal.12247.
Lang, P., Behavioral treatment and bio-behavioral assessment: Computer applications. Technol. Mental Health Care Deliv. Syst, 119–137, 1980.
E. Sutoyo, A. Almaarif, and I. T. R. Yanto, “Sentiment Analysis of Student Evaluations of Teaching Using Deep Learning Approach,” Lecture Notes
in Networks and Systems, pp. 272–281, 2021, doi: 10.1007/978-3-030-80216-5_20.
Arulkumar V, Vinod D, Devipriya A, Chemmalar Selvi G, Surendran S and Mohammad Arif, “Monitoring and Recognition of Heart Health using
Heartbeat Classification with Deep Learning and IoT, Journal of Machine and Computing, vol.3, no.3, pp. 326-339, July 2023. doi:
10.53759/7669/jmc202303028.
Y. Dyulicheva, “Learning Analytics in MOOCs as an Instrument for Measuring Math Anxiety,” Voprosy Obrazovaniya / Educational Studies
Moscow, no. 4, pp. 243–265, Dec. 2021, doi: 10.17323/1814-9545-2021-4-243-265.
X. Li, H. Zhang, Y. Ouyang, X. Zhang, and W. Rong, “A Shallow BERT-CNN Model for Sentiment Analysis on MOOCs Comments,” 2019 IEEE
International Conference on Engineering, Technology and Education (TALE), Dec. 2019, doi: 10.1109/tale48000.2019.9225993.
D. Pramod, S. Vijayakumar Bharathi, and R. Raman, “Faculty Effectiveness Prediction using Machine Learning and Text Analytics,” 2022 IEEE
Technology and Engineering Management Conference (TEMSCON EUROPE), Apr. 2022, doi: 10.1109/temsconeurope54743.2022.9801997.
ONAN, “Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach,” Computer Applications in
Engineering Education, vol. 29, no. 3, pp. 572–589, May 2020, doi: 10.1002/cae.22253.
S. Hussain et al., “Aspect2Labels: A novelistic decision support system for higher educational institutions by using multi-layer topic modelling
approach,” Expert Systems with Applications, vol. 209, p. 118119, Dec. 2022, doi: 10.1016/j.eswa.2022.118119.
P. K. Singh and S. Paul, “Deep Learning Approach for Negation Handling in Sentiment Analysis,” IEEE Access, vol. 9, pp. 102579–102592, 2021,
doi: 10.1109/access.2021.3095412.
Acknowledgements
Authors thank Reviewers for taking the time and effort necessary to review the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Smitha B A and Raja Praveen K N, “ORDSAENet: Outlier Resilient Semantic Featured Deep Driven Sentiment Analysis Model for Education Domain”, Journal of Machine and Computing, vol.3, no.4, pp. 408-430, October 2023. doi: 10.53759/7669/jmc202303034.
