Sentiment analysis and opinion mining on educational data: A survey

SMOTE synthetic minority over-sampling technique

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865 · see citing works

Latent dirichlet allocation

(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337 · see citing works

Latent Dirichlet Allocation

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.003.0082 · Cited by 12,649 · see citing works

VADER: a parsimonious rule-based model for sentiment analysis of social media text

Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14550 · Cited by 6,087 · see citing works

ADASYN: Adaptive synthetic sampling approach for imbalanced learning

He, H., Bai, Y., Garcia, E. A., & Shutao Li, A. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4633969 · Cited by 4,945 · see citing works

Sentiment Analysis and Opinion Mining

Liu, B. (2012) · Synthesis lectures on human language technologies · doi:10.2200/s00416ed1v01y201204hlt016 · Cited by 3,837 · see citing works

Recognizing contextual polarity in phrase-level sentiment analysis

Wilson, T., Wiebe, J., & Hoffmann, P. (2005) · doi:10.3115/1220575.1220619 · Cited by 3,388 · see citing works

CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON

Mohammad, S. M., & Turney, P. D. (2012) · Computational Intelligence · doi:10.1111/j.1467-8640.2012.00460.x · Cited by 2,757 · see citing works

SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining

Baccianella, S., Esuli, A., & Sebastiani, F. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2jktnrykui9s · Cited by 2,691 · see citing works

What's basic about basic emotions?

Ortony, A., & Turner, T. J. (1990) · Psychological Review · doi:10.1037/0033-295x.97.3.315 · Cited by 2,015 · see citing works

Sentiment Analysis and Opinion Mining

Liu, B. (2012) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-02145-9 · Cited by 1,872 · see citing works

The Development and Psychometric Properties of LIWC2015

Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015) · University of Texas at Austin · doi:10.15781/t25p41 · Cited by 1,674 · see citing works

A Survey on Deep Learning for Named Entity Recognition

Li, J., Sun, A., Han, J., & Li, C. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2981314 · Cited by 1,667 · see citing works

Predicting the semantic orientation of adjectives

Hatzivassiloglou, V., & McKeown, K. R. (1997) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/976909.979640 · Cited by 1,445 · see citing works

The General Inquirer: A Computer Approach to Content Analysis

Green, B. F., Stone, P. J., Dunphy, D. C., Smith, M. S., & Ogilvie, D. M. (1967) · American Educational Research Journal · doi:10.2307/1161774 · Cited by 1,013 · see citing works

NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of TweetsPreprint

Mohammad, S. M., Kiritchenko, S., & Zhu, X. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1308.6242 · Cited by 812 · see citing works

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

(2008) · doi:10.1109/ijcnn13382.2008 · Cited by 784 · see citing works

A new ANEW: Evaluation of a word list for sentiment analysis in microblogsPreprint

Nielsen, F. Å. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.2903 · Cited by 733 · see citing works

Transformer models for text-based emotion detection: a review of BERT-based approaches

Acheampong, F. A., Nunoo‐Mensah, H., & Chen, W. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09958-2 · Cited by 686 · see citing works

2020 IEEE International Conference on Big Data (Big Data)

(2020) · doi:10.1109/bigdata50022.2020 · Cited by 663 · see citing works

Proceedings of the 36th annual meeting on Association for Computational Linguistics -

(1998) · doi:10.3115/980845 · Cited by 636 · see citing works

Exploring Opportunities and Challenges of Artificial Intelligence and Machine Learning in Higher Education Institutions

Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O. M. D., Păun, D., & Mihoreanu, L. (2021) · Sustainability · doi:10.3390/su131810424 · Cited by 561 · see citing works

OpinionFinder

Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J. S., Wiebe, J., Choi, Y., Cardie, C., Riloff, E., & Patwardhan, S. (2005) · doi:10.3115/1225733.1225751 · Cited by 469 · see citing works

Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach

Onan, A. (2020) · Computer Applications in Engineering Education · doi:10.1002/cae.22253 · Cited by 400 · see citing works

Systematic reviews in sentiment analysis: a tertiary study

Ligthart, A., Catal, C., & Teki̇nerdoğan, B. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09973-3 · Cited by 340 · see citing works

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

Shaik, T., Tao, X., Li, Y., Dann, C. E., McDonald, J., Redmond, P., & Galligan, L. (2022) · IEEE Access · doi:10.1109/access.2022.3177752 · Cited by 333 · see citing works

Sentiment Analysis: Adjectives and Adverbs are better than Adjectives Alone

Benamara, F., Cesarano, C., Picariello, A., Recupero, D. R., & Subrahmanian, V. S. (2007) · Cited by 331 · see citing works

Advances in Intelligent Systems and Computing

Deshpande, P. S., Abraham, A., Iyer, B., & Ma, K. (2021) · Cited by 290 · see citing works

A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach

Zhao, H., Liu, Z., Yao, X., & Yang, Q. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102656 · Cited by 237 · see citing works

Weakly Supervised Framework for Aspect-Based Sentiment Analysis on Students’ Reviews of MOOCs

Kastrati, Z., Imran, A. S., & Kurti, A. (2020) · IEEE Access · doi:10.1109/access.2020.3000739 · Cited by 175 · see citing works

BiLSTM with Multi-Polarity Orthogonal Attention for Implicit Sentiment Analysis

Wei, J., Liao, J., Yang, Z., Wang, S., & Zhao, Q. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.11.054 · Cited by 159 · see citing works

Flipped Classroom Strategies for CS Education

Maher, M. L., Latulipe, C., Lipford, H. R., & Rorrer, A. S. (2015) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/2676723.2677252 · Cited by 147 · see citing works

Sentiment analysis in education research: a review of journal publications

Zhou, J., & Ye, J.-M. (2020) · Interactive Learning Environments · doi:10.1080/10494820.2020.1826985 · Cited by 143 · see citing works

Lexicon-Based vs. Bert-Based Sentiment Analysis: A Comparative Study in Italian

Catelli, R., Pelosi, S., & Esposito, M. (2022) · Electronics · doi:10.3390/electronics11030374 · Cited by 133 · see citing works

Advance organizers in flipped classroom via e-learning management system and the promotion of integrated science process skills

Elfeky, A. I. M., Masadeh, T. S. Y., & Elbyaly, M. Y. H. (2019) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2019.100622 · Cited by 132 · see citing works

Structuring Flipped Classes with Lightweight Teams and Gamification

Latulipe, C., Long, N. B., & Seminario, C. E. (2015) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/2676723.2677240 · Cited by 128 · see citing works

Sentiment analysis on tweets for social events

Zhou, X., Tao, X., Yong, J., & Yang, Z. (2013) · Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd.2013.6581022 · Cited by 124 · cited 1× · see citing works

Proceedings of the 47th ACM Technical Symposium on Computing Science Education

(2016) · doi:10.1145/2839509 · Cited by 118 · see citing works

Compositionality Principle in Recognition of Fine-Grained Emotions from Text

Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2009) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v3i1.13987 · Cited by 116 · see citing works

Key factors in MOOC pedagogy based on NLP sentiment analysis of learner reviews: What makes a hit

Li, L., Johnson, J. B., Aarhus, W., & Shah, D. (2021) · Computers & Education · doi:10.1016/j.compedu.2021.104354 · Cited by 114 · see citing works

2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops

(2009) · doi:10.1109/acii15064.2009 · Cited by 108 · see citing works

Entity-level sentiment analysis of issue comments

Ding, J. J., Sun, H., Wang, X., & Liu, X. (2018) · doi:10.1145/3194932.3194935 · Cited by 102 · see citing works

KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis

D’Aniello, G., Gaeta, M., & Rocca, I. L. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10134-9 · Cited by 100 · see citing works

2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

Mangialavori, M., Pompei, L., Nardecchia, F., Bisegna, F., Fichera, A., Rizzo, G., Tronchin, L., & Schibuola, L. (2020) · doi:10.1109/ecti-con49241.2020 · Cited by 92 · see citing works

Improving early prediction of academic failure using sentiment analysis on self‐evaluated comments

Yu, L.-C., Lee, C. W., Pan, H. I., Chou, C., Chao, P.-Y., Chen, Z., Tseng, S. F., Chan, C., & Lai, K.-H. R. (2018) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12247 · Cited by 91 · see citing works

Sentiment Analysis using SVM: A Systematic Literature Review

Ahmad, M., Aftab, S., Salman, M., & Hameed, N. (2018) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2018.090226 · Cited by 87 · see citing works

Proceedings of the 45th ACM technical symposium on Computer science education

Dougherty, J., Nagel, K., Decker, A., & Eiselt, K. (2014) · doi:10.1145/2538862 · Cited by 86 · see citing works

SentiFul: Generating a reliable lexicon for sentiment analysis

Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2009) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2009.5349575 · Cited by 74 · see citing works

Text analytics approach to extract course improvement suggestions from students’ feedback

Gottipati, S., Shankararaman, V., & Lin, J. R. (2018) · Research and Practice in Technology Enhanced Learning · doi:10.1186/s41039-018-0073-0 · Cited by 71 · see citing works

Semantic Analysis of Learners’ Emotional Tendencies on Online MOOC Education

Wang, L., Hu, G., & Zhou, T. H. (2018) · Sustainability · doi:10.3390/su10061921 · Cited by 71 · see citing works

Aspect based sentiment analysis of students opinion using machine learning techniques

Sivakumar, M., & Reddy, U. S. (2017) · International Conference on Inventive Computing and Informatics (ICICI) · doi:10.1109/icici.2017.8365231 · Cited by 68 · see citing works

Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums

Liu, Z., Yang, C., Rüdian, S., Liu, S., Zhao, L., & Wang, T. (2019) · Interactive Learning Environments · doi:10.1080/10494820.2019.1610449 · Cited by 68 · see citing works

FedStack: Personalized activity monitoring using stacked federated learning

Shaik, T., Tao, X., Higgins, N., Gururajan, R., Li, Y., Zhou, X., & Acharya, U. R. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109929 · Cited by 68 · see citing works

Syntactic, Semantic and Sentiment Analysis: The Joint Effect on Automated Essay Evaluation

Janda, H. K., Pawar, A., Du, S., & Mago, V. (2019) · IEEE Access · doi:10.1109/access.2019.2933354 · Cited by 67 · see citing works

Learning Latent Sentiment Scopes for Entity-Level Sentiment Analysis

Li, H., & Lu, W. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11016 · Cited by 66 · see citing works

Detection of spam reviews: a sentiment analysis approach

Saumya, S., & Singh, J. P. (2018) · CSI Transactions on ICT · doi:10.1007/s40012-018-0193-0 · Cited by 64 · see citing works

Aspect-based sentiment analysis of reviews in the domain of higher education

Nikolić, N., Grljević, O., & Kovačević, A. (2020) · The Electronic Library · doi:10.1108/el-06-2019-0140 · Cited by 60 · see citing works

Sentiment Analysis for Distance Education Course Materials: A Machine Learning Approach

Osmanoğlu, U. Ö., Atak, O. N., ÇAĞLAR, K., KAYHAN, H., & Can, T. C. (2020) · Journal of Educational Technology and Online Learning · doi:10.31681/jetol.663733 · Cited by 56 · see citing works

Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design

Shen, W., Li, W., BARTHES, J.-P. A., Luo, J., Zhu, H., Yong, J., & Li, X. (2013) · HAL · doi:10.1109/cscwd30294.2013 · Cited by 54 · see citing works

How does BERT capture semantics? A closer look at polysemous words

Yenicelik, D., Schmidt, F., & Kilcher, Y. (2020) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2020.blackboxnlp-1.15 · Cited by 51 · see citing works

Online critical review classification in response strategy and service provider rating: Algorithms from heuristic processing, sentiment analysis to deep learning

Zhu, J., Chang, Y., Ku, C., Li, S. Y., & Chen, C. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.11.007 · Cited by 50 · see citing works

Conciseness is better: Recurrent attention LSTM model for document-level sentiment analysis

Zhang, Y., Wang, J., & Zhang, X. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.072 · Cited by 50 · see citing works

Learning Review Representations from user and Product Level Information for Spam Detection

Yuan, C., Zhou, W. J., Ma, Q., Lv, S., Han, J., & Hu, S. (2019) · Proceedings · doi:10.1109/icdm.2019.00188 · Cited by 48 · see citing works

A review on sentiment discovery and analysis of educational big‐data

Han, Z., Wu, J., Huang, C., Huang, Q., & Zhao, M. (2019) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1328 · Cited by 44 · see citing works

Deep Learning Approach for Negation Handling in Sentiment Analysis

Singh, P. K., & Paul, S. (2021) · IEEE Access · doi:10.1109/access.2021.3095412 · Cited by 42 · see citing works

Incorporating sentimental analysis into development of a hybrid classification model

Kaur, C., Boush, M. S. A., Hassen, S. M., Hakami, W. A., Abdalraheem, M. H. O., Galam, N. M., Hadi, N. A., Hadi, N. A., & Benjeed, A. O. S. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns1.4924 · Cited by 42 · see citing works

The Potential of Machine Learning Algorithms for Sentiment Classification of Students’ Feedback on MOOC

Edalati, M., Imran, A. S., Kastrati, Z., & Daudpota, S. M. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-82199-9_2 · Cited by 41 · see citing works

Adaptive cross-contextual word embedding for word polysemy with unsupervised topic modeling

Li, S., Pan, R., Luo, H., Liu, X., & Zhao, G. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106827 · Cited by 40 · see citing works

A Sentiment Analysis Model for Faculty Comment Evaluation Using Ensemble Machine Learning Algorithms

Lalata, J.-A. P., Gerardo, B. D., & Medina, R. P. (2019) · doi:10.1145/3341620.3341638 · Cited by 39 · see citing works

Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities

Tao, X., Shaik, T., Higgins, N., Gururajan, R., & Zhou, X. (2021) · Sensors · doi:10.3390/s21030776 · Cited by 37 · see citing works

Feature-Based Twitter Sentiment Analysis With Improved Negation Handling

Gupta, I., & Joshi, N. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3069413 · Cited by 37 · see citing works

Sentiment Analysis for Depression Detection on Social Networks

Tao, X., Zhou, X., Zhang, J., & Yong, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49586-6_59 · Cited by 36 · see citing works

Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

Alishahi, A., Belinkov, Y., Chrupała, G., Hupkes, D., Pinter, Y., & Sajjad, H. (2020) · doi:10.18653/v1/2020.blackboxnlp-1 · Cited by 35 · see citing works

A knowledge retrieval model using ontology mining and user profiling

Tao, X., Li, Y., & Nayak, R. (2008) · Integrated Computer-Aided Engineering · doi:10.3233/ica-2008-15404 · Cited by 33 · see citing works

Palaute: An Online Text Mining Tool for Analyzing Written Student Course Feedback

Grönberg, N., Knutas, A., Hynninen, T., & Hujala, M. (2021) · IEEE Access · doi:10.1109/access.2021.3116425 · Cited by 33 · see citing works

Aspect2Labels: A novelistic decision support system for higher educational institutions by using multi-layer topic modelling approach

Hussain, S., Ayoub, M., Jilani, G., Yu, Y., Khan, A., Wahid, J. A., Butt, M. F. A., Yang, G., Möller, D. P. F., & Hou, W. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118119 · Cited by 32 · see citing works

Feedback Analysis in Outcome Base Education Using Machine Learning

Lwin, H. H., Oo, S., Ye, K. Z., Lin, K. K., Aung, W. P., & Ko, P. P. (2020) · International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) · doi:10.1109/ecti-con49241.2020.9158328 · Cited by 27 · see citing works

SUFAT - An Analytics Tool for Gaining Insights from Student Feedback Comments

Pyasi, S., Gottipati, S., & Shankararaman, V. (2018) · 2022 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie.2018.8658457 · Cited by 26 · see citing works

Polarity, emotions and online activity of students and tutors as features in predicting grades

Gkontzis, A. F., Kotsiantis, S. B., Kalles, D., Panagiotakopoulos, C. T., & Verykios, V. S. (2020) · Intelligent Decision Technologies · doi:10.3233/idt-190137 · Cited by 22 · see citing works

Aspect-Based Emotion Analysis on Speech for Predicting Performance in Collaborative Learning

Dehbozorgi, N., & Mohandoss, D. P. (2021) · 2022 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie49875.2021.9637330 · Cited by 22 · see citing works

Exploring Lightweight Teams in a Distributed Learning Environment

MacNeil, S., Latulipe, C., Long, B., & Yadav, A. K. (2016) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/2839509.2844577 · Cited by 21 · see citing works

A Shallow BERT-CNN Model for Sentiment Analysis on MOOCs Comments

Li, X., Zhang, H., Ouyang, Y., Zhang, X., & Rong, W. (2019) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale48000.2019.9225993 · Cited by 20 · see citing works

Koli Calling '20: Proceedings of the 20th Koli Calling International Conference on Computing Education Research

Ihantola, P., Leinonen, J., & Rintala, M. (2020) · doi:10.1145/3428029 · Cited by 19 · see citing works

Constructing automatic domain-specific sentiment lexicon using KNN search via terms discrimination vectors

Alqasemi, F. A., Abdelwahab, A., & Abdelkader, H. M. (2017) · International Journal of Computers and Applications · doi:10.1080/1206212x.2017.1409477 · Cited by 17 · see citing works

Evaluation and Summarization of Student Feedback Using Sentiment Analysis

Sharma, N., & Jain, V. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-3383-9_35 · Cited by 16 · see citing works

Sentiment classification techniques for tracking literary reputation

Taboada, M., Gillies, M. A., & McFetridge, P. (2006) · Cited by 15 · see citing works

TWE‐WSD: An effective topical word embedding based word sense disambiguation

Jia, L., Tang, J., Li, M., You, J., Ding, J., & Chen, Y. (2021) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12006 · Cited by 15 · see citing works

Advanced Data Mining and Applications

Sun, A., Li, C., Zhang, W. E., Peng, W.-C., & Cong, G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-69179-4 · Cited by 14 · see citing works

Sentiment Analysis of Student Evaluations of Teaching Using Deep Learning Approach

Sutoyo, E., Almaarif, A., & Yanto, I. T. R. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-80216-5_20 · Cited by 14 · see citing works

Incentivizing Full-Time Enrollment at Community Colleges: What Influences Students’ Decision to Take More Courses?

McKinney, L., Burridge, A. B., Lee, M. M., Bourdeau, G. V., & Miller-Waters, M. (2022) · Community College Review · doi:10.1177/00915521211061416 · Cited by 14 · see citing works

Opinion Mining from Student Feedback Data Using Supervised Learning Algorithms

Bansal, M., Verma, S., Vig, K., & Kakran, K. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-12413-6_32 · Cited by 14 · see citing works

Machine Learning and Deep Learning for Sentiment Analysis Over Students' Reviews: An Overview StudyPreprint

Yang, R. (2021) · Preprints.org · doi:10.20944/preprints202102.0108.v1 · Cited by 13 · see citing works

Opinion spam detection using Deep Learning

Bathla, G., & Kumar, A. (2021) · International Conference on Signal Processing and Integrated Networks (SPIN) · doi:10.1109/spin52536.2021.9566048 · Cited by 12 · see citing works

Sentiment analysis on educational datasets: a comparative evaluation of commercial tools

Dolianiti, F. S., Iakovakis, D., Dias, S. B., Hadjileontiadou, S. J., Diniz, J. A., Natsiou, G., Tsitouridou, M., Bamidis, P. D., & Hadjileontiadis, L. J. (2019) · doi:10.26220/une.2987 · Cited by 11 · see citing works

Predicting Students’ Satisfaction Towards Online Courses Using Aspect-Based Sentiment Analysis

Rosalind, J. M., & Suguna, S. (2022) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-11633-9_3 · Cited by 11 · see citing works

Latent Dirichlet Allocation for textual student feedback analysis

Gottipati, S., Shankararaman, V., & Lin, J. R. (2018) · International Conference on Computers in Education · Cited by 10 · see citing works

Interpretation of Sentiment Analysis with Human-in-the-Loop

Yeruva, V. K., Chandrashekar, M., Lee, Y., Rydberg-Cox, J. A., Blanton, V., & Oyler, N. A. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378221 · Cited by 10 · see citing works

Innovations in Bio-Inspired Computing and Applications

Abraham, A., Bajaj, A., Gandhi, N., Madureira, A. M., & Kahraman, C. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-27499-2 · Cited by 10 · see citing works

Emotion Detection and Opinion Mining from Student Comments for Teaching Innovation Assessment

Tzacheva, A. A., & Easwaran, A. (2021) · International Journal of Education (IJE) · doi:10.5121/ije2021.9203 · Cited by 9 · see citing works

UAE e-Learning Sentiment Analysis Framework

Wehbe, D., Alhammadi, A., Almaskari, H., Alsereidi, K., & Ismail, H. (2021) · International Conference on Arab Women in Computing (ArabWIC) · doi:10.1145/3485557.3485570 · Cited by 9 · see citing works

Third International Conference on Image Processing and Capsule Networks

(2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-12413-6 · Cited by 9 · see citing works

Exploring Learner Engagement Patterns in Teach-Outs Using Topic, Sentiment and On-topicness to Reflect on Pedagogy

Yan, W., Dowell, N., Holman, C., Welsh, S. S., Choi, H., & Brooks, C. A. (2019) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3303772.3303836 · Cited by 8 · see citing works

Word Sense Disambiguation for 158 Languages using Word Embeddings Only

Logacheva, V., Тесленко, Д., Shelmanov, A., Remus, S., Ustalov, D., Kutuzov, A., Artemova, E., Biemann, C., Ponzetto, S. P., & Panchenko, A. (2020) · MADOC (University of Mannheim) · doi:10.48550/arxiv.2003.06651 · Cited by 8 · see citing works

Proceedings of the 3rd International Workshop on Emotion Awareness in Software Engineering

(2018) · doi:10.1145/3194932 · Cited by 8 · see citing works

Faculty Effectiveness Prediction using Machine Learning and Text Analytics

Pramod, D., Bharathi, S. V., & Raman, R. (2022) · IEEE Technology and Engineering Management Conference (TEMSCON) · doi:10.1109/temsconeurope54743.2022.9801997 · Cited by 8 · see citing works

Building Pedagogical Conversational Agents, Affectively Correct

Feidakis, M., Kasnesis, P., Giatraki, E., Giannousis, C., Patrikakis, C. Z., & Monachelis, P. (2019) · International Conference on Computer Supported Education · doi:10.5220/0007771001000107 · Cited by 7 · see citing works

An online tool for analyzing written student feedback

Grönberg, N., Knutas, A., Hynninen, T., & Hujala, M. (2020) · Koli Calling International Conference on Computing Education Research · doi:10.1145/3428029.3428565 · Cited by 7 · see citing works

Automated Discussion Analysis - Framework for Knowledge Analysis from Class Discussions

Gottipati, S., Shankararaman, V., & GOKARN, M. N. (2020) · 2022 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie44824.2020.9273819 · Cited by 7 · see citing works

Computer, Communication, and Signal Processing

Neuhold, E. J., Fernando, X. N., Lu, J., Piramuthu, S., & Chandrabose, A. (2022) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-11633-9 · Cited by 7 · see citing works

THE ORIENTATION OF USER OPINIONS THROUGH ADVERBS, VERBS AND NOUNS

Vermeij, M. J. M. (2008) · Cited by 6 · see citing works

International Conference on Emerging Applications and Technologies for Industry 4.0 (EATI’2020)

(2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-80216-5 · Cited by 6 · see citing works

A Sentiment-Based Approach to Predict Learners’ Perceptions Towards YouTube Educational Videos

Faizi, R. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-96299-9_52 · Cited by 6 · see citing works

2022 IEEE Technology and Engineering Management Conference (TEMSCON EUROPE)

(2022) · doi:10.1109/temsconeurope54743.2022 · Cited by 6 · see citing works

SETSum: Summarization and Visualization of Student Evaluations of Teaching

Hu, Y., Zhang, S., Sathy, V., Panter, A. T., & Bansal, M. (2022) · doi:10.18653/v1/2022.naacl-demo.9 · Cited by 6 · see citing works

Proceedings of the 2019 International Conference on Big Data Engineering

(2019) · doi:10.1145/3341620 · Cited by 3 · see citing works

Analyzing Pangasinan State University Student’s FacultyTeaching Performance Rating Using Text Mining Technique

Roaring, B. F., Patacsil, F. F., & Parrone, J. M. (2022) · WSEAS Transactions on Information Science and Applications archive · doi:10.37394/23209.2022.19.16 · Cited by 3 · see citing works

Learning review representations from user and product level information for spam detectionPreprint

Yuan, C., Zhou, W. J., Ma, Q., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.04455 · Cited by 1 · see citing works

Large language models (LLMs): survey, technical frameworks, and future challenges

Kumar, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10888-y · Cited by 395 · see citing works

Sentiment analysis methods, applications, and challenges: A systematic literature review

Mao, Y., Liu, Q., & Zhang, Y. (2024) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2024.102048 · Cited by 286 · see citing works

A Large Language Model Approach to Educational Survey Feedback Analysis

Parker, M. J., Anderson, C., Stone, C., & Oh, Y. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00414-0 · Cited by 86 · see citing works

DASKT: A Dynamic Affect Simulation Method for Knowledge Tracing

Sun, X., Zhang, K., Liu, Q., Shen, S., Wang, F., Guo, Y., & Chen, E. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3526584 · Cited by 43 · see citing works

Educational data mining: a 10-year review

Kalita, E., Oyelere, S. S., Gaftandzhieva, S. N., Rajesh, K. N. V. P. S., Jagatheesaperumal, S. K., Mohamed, A. S. A., Elbarawy, Y. M., Desuky, A. S., Hussain, S., Çifçi, M. A., et al. (2025) · Discover Computing · doi:10.1007/s10791-025-09589-z · Cited by 66 · see citing works

Bridging human resource development processes through generative Artificial Intelligence

Korzyński, P., Kim, S., & Egan, T. M. (2024) · Human Resource Development Quarterly · doi:10.1002/hrdq.21551 · Cited by 35 · see citing works

Assessing Accuracy: A Study of Lexicon and Rule-Based Packages in R and Python for Sentiment Analysis

Mahmoudi, A., Jemielniak, D., & Ciechanowski, L. (2024) · IEEE Access · doi:10.1109/access.2024.3353692 · Cited by 32 · see citing works

Using data science for sustainable development in higher education

Filho, W. L., Eustachio, J. H. P. P., Nita, A. C., Dinis, M. A. P., Salvia, A. L., Cotton, D., Frizzo, K., Trevisan, L. V., & Dibbern, T. (2023) · Sustainable Development · doi:10.1002/sd.2638 · Cited by 29 · see citing works

ORDSAENet: Outlier Resilient Semantic Featured Deep Driven Sentiment Analysis Model for Education Domain

A, S. B., & N, R. P. K. (2023) · Journal of Machine and Computing · doi:10.53759/7669/jmc202303034 · Cited by 24 · see citing works

Predicting Online Education Dropout: A new Machine Learning Model based on Sentiment Analysis, Socio-demographic, and Behavioral Data

Zerkouk, M., Mihoubi, M., Chikhaoui, B., & Wang, S. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00472-y · Cited by 22 · see citing works

An empirical study of sentiment analysis utilizing machine learning and deep learning algorithms

Erkantarcı, B., & Bakal, G. (2023) · Journal of Computational Social Science · doi:10.1007/s42001-023-00236-5 · Cited by 21 · see citing works

Expanding AI’s Role in Healthcare Applications: A Systematic Review of Emotional and Cognitive Analysis Techniques

Nag, P. K., Bhagat, A., & Priya, R. (2025) · IEEE Access · doi:10.1109/access.2025.3562131 · Cited by 24 · see citing works

Application of SVM and Chi-Square Feature Selection for Sentiment Analysis of Indonesia’s National Health Insurance Mobile Application

Hokijuliandy, E., Napitupulu, H., & Firdaniza, F. (2023) · Mathematics · doi:10.3390/math11173765 · Cited by 19 · see citing works

AI and big data-driven decision support for fostering student innovation in music education at private underground colleges

Liu, L. (2023) · Journal of Information Systems Engineering & Management · doi:10.55267/iadt.07.13840 · Cited by 19 · see citing works

Predicting Facebook sentiments towards research

Shahzad, M., Freeman, C., Rahimi, M., & Alhoori, H. (2023) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100010 · Cited by 21 · see citing works

ChatReview: A ChatGPT-enabled natural language processing framework to study domain-specific user reviews

Ho, B., Mayberry, T., Nguyen, K. L., Dhulipala, M., & Pallipuram, V. K. (2023) · Machine Learning with Applications · doi:10.1016/j.mlwa.2023.100522 · Cited by 18 · see citing works

Sentiment Analysis on Social Media Against Public Policy Using Multinomial Naive Bayes

Zulfikar, W. B., Atmadja, A. R., & Pratama, S. F. (2023) · Scientific Journal of Informatics · doi:10.15294/sji.v10i1.39952 · Cited by 18 · see citing works

Linking short- and long-term impacts of the government, consumers, and manufacturers on NEV sales and market share in China

Jing, P., Shao, D., Liu, Y., Chen, Y., & Zhang, S. (2024) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2024.104090 · Cited by 17 · see citing works

Predictive deep reinforcement learning with multi-agent systems for adaptive time series forecasting

Shaik, T., Tao, X., Li, L., Xie, H., Acharya, U. R., Gururajan, R., & Zhou, X. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113941 · Cited by 15 · see citing works

A Hybrid Text Summarization Technique of Student Open-Ended Responses to Online Educational Surveys

Καρούσος, Ν., Vorvilas, G., Pantazi, D., & Verykios, V. S. (2024) · Electronics · doi:10.3390/electronics13183722 · Cited by 13 · see citing works

Exploring Factors Influencing Teachers' Acceptance of AI Tools for Creating Animated Educational Videos With Pedagogical Agents

Malakul, S. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70083 · Cited by 12 · see citing works

Sentiment analysis of student feedback using attention-based RNN and transformer embedding

Zyout, I., & Zyout, M. (2024) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v13.i2.pp2173-2184 · Cited by 12 · see citing works

FRACTAL-INSPIRED SENTIMENT ANALYSIS: EVALUATION OF LARGE LANGUAGE MODELS AND DEEP LEARNING METHODS

AlSagri, H. S., & Sohail, S. S. (2024) · Fractals · doi:10.1142/s0218348x24400565 · Cited by 12 · see citing works

Advancing sentiment analysis for low-resourced african languages using pre-trained language models

Mabokela, K. R., Primus, M., & Çelik, T. (2025) · PLoS ONE · doi:10.1371/journal.pone.0325102 · Cited by 12 · see citing works

LLMs in Education: Evaluation GPT and BERT Models in Student Comment Classification

Pilicita, A., & Barra, E. (2025) · Multimodal Technologies and Interaction · doi:10.3390/mti9050044 · Cited by 11 · see citing works

Artificial intelligence and validity

Azzam, T. (2023) · New Directions for Evaluation · doi:10.1002/ev.20565 · Cited by 11 · see citing works

Assessing Teacher Competencies in Higher Education: A Sentiment Analysis of Student Feedback

Dervenis, C. (2024) · International Journal of Information and Education Technology · doi:10.18178/ijiet.2024.14.4.2074 · Cited by 10 · see citing works

Sentiment Diffusion in Online Social Networks: A Survey from the Computational Perspective

Han, X., Xu, M., Deng, X., & Wang, B. (2025) · ACM Computing Surveys · doi:10.1145/3736750 · Cited by 11 · see citing works

Text Analytics and Natural Language Processing for Business Insights: A Comprehensive Review

Sinjanka, Y. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.55893 · Cited by 10 · see citing works

A critical review of data mining in education on the levels and aspects of education

Inusah, F., & Missah, Y. M. (2024) · Quality Education for All · doi:10.1108/qea-01-2024-0006 · Cited by 8 · see citing works

Cross-cultural perspectives on entrepreneurship training effectiveness: understanding the role of training duration, methodology, and expertise

Anwar, R. S., Ahmed, R. R., Štreimikienė, D., & Štreimikis, J. (2025) · International Entrepreneurship and Management Journal · doi:10.1007/s11365-025-01099-0 · Cited by 7 · see citing works

A podcast to teach medical humanities at medical school: a text-mining study of students’ lived experience

Roze, E. F., Nilles, C., Louapre, C., Soumet-Leman, B., Renaud, M.-C., Dechartres, A., & Atkinson-Clément, C. (2024) · Medical Education Online · doi:10.1080/10872981.2024.2367823 · Cited by 7 · see citing works

Improving Multi-label Classification Performance on Imbalanced Datasets Through SMOTE Technique and Data Augmentation Using IndoBERT Model

Cahya, L. D., Luthfiarta, A., Krisna, J. I. T., Winarno, S., & Nugraha, A. (2024) · Jurnal Nasional Teknologi dan Sistem Informasi · doi:10.25077/teknosi.v9i3.2023.290-298 · Cited by 7 · see citing works

Exploring emotional dynamics: Word count effects in Facebook posts by sports shoe brands

Hossain, M. S., Babu, M. A., & Yusuf, K. M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e39808 · Cited by 6 · see citing works

Generative adversarial network-based phishing URL detection with variational autoencoder and transformer

Sasi, J. K., & Balakrishnan, A. (2024) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v13.i2.pp2165-2172 · Cited by 6 · see citing works

Opinion mining of professors' sentiments towards the adoption of technology, the latest teaching trends, and their effectiveness

Mahrishi, M., Rodriguez-Medellin, C. E., Flores, M. D. R. Y., Brito, M. P. G., & Abbas, A. (2024) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2024.101249 · Cited by 6 · see citing works

Dynamic Text Augmentation for Robust Sentiment Analysis: Enhancing Model Performance With EDA and Multi-Channel CNN

Trisna, K. W., Huang, J., Chen, Y., & Putra, I. G. J. E. (2025) · IEEE Access · doi:10.1109/access.2025.3538621 · Cited by 7 · see citing works

Data Augmentation For Sorani Kurdish News Headline Classification Using Back-Translation And Deep Learning Model

Badawi, S. S. (2023) · Kurdistan Journal of Applied Research · doi:10.24017/science/2023.1.4 · Cited by 6 · see citing works

Comparing Random Forest and XGBoost for Sentiment Classification of Student Social Media Posts: A Case Study on Pre-Board Exam Stress

Saklani, S., Manchanda, M., Bisht, G., & Thapa, K. (2025) · Global Conference in Emerging Technology (GINOTECH) · doi:10.1109/ginotech63460.2025.11076620 · Cited by 5 · see citing works

Data Mining to Identify University Student Dropout Factors

Marín, Y. R., Huatangari, L. Q., Caro, O. C., Guevara, J. L. M., Tuesta, J. N. A., Bardales, E. S., & Santos, R. C. (2025) · Applied Sciences · doi:10.3390/app152211911 · Cited by 5 · see citing works

Application of Sentiment Analysis in Diverse Domains

Sweta, S. (2024) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-981-97-2474-1_2 · Cited by 5 · see citing works

Evaluating the quality of digital education resources based on learners’ online reviews through topic modeling and opinion mining

Zhang, L., Jiang, Q., Xiong, W., & Zhao, W. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13407-w · Cited by 5 · see citing works

The role of AI in shaping educational experiences in computer science: A systematic review

Golrang, A., & Sharma, K. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100199 · Cited by 5 · see citing works

Machine Learning Models for Sentiment Analysis: State of the Art and Applications

Anwar, K., Garg, A., Ahmad, S., & Wasid, M. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10726197 · Cited by 5 · see citing works

Student Perspectives on Teacher Interpersonal Behaviours in AI ‐Mediated Higher Education: A Q Methodology Study

Jalilzadeh, K., Çelık, F., & Duran, V. (2025) · European Journal of Education · doi:10.1111/ejed.70293 · Cited by 5 · see citing works

SentXFormer: a transformer-enhanced hybrid deep learning framework for cross-domain sentiment analysis of customer reviews

Kumar, A., Abhishek, K., & M, A. S. B. (2025) · Scientific Reports · doi:10.1038/s41598-025-33526-1 · Cited by 4 · see citing works

A Context-Aware Framework for Sentiment Analysis of Student Feedback to Inform Educational Strategies in Latin America

Pineda-Briseño, A., Oblitas, J., Sánchez, L. C., Gómez, W. A. S., & Baltazar, R. (2026) · Education Sciences · doi:10.3390/educsci16030399 · Cited by 4 · see citing works

Determining Intent: Sentiment Analysis Based on the Classification of Indonesian Tourist Destination Review Texts

Muhammad, A., Defit, S., & Nurcahyo, G. W. (2024) · Journal of Advances in Information Technology · doi:10.12720/jait.15.10.1106-1116 · Cited by 4 · see citing works

Affective knowledge assisted bi-directional learning for Multi-modal Aspect-based Sentiment Analysis

Shi, X., Yang, M., Hu, M. Q., Ren, F., Kang, X., & Ding, W. (2024) · Computer Speech & Language · doi:10.1016/j.csl.2024.101755 · Cited by 4 · see citing works

AI-driven educational transformation in ICT: Improving adaptability, sentiment, and academic performance with advanced machine learning

Imran, A., Li, J., & Alshammari, A. (2025) · PLoS ONE · doi:10.1371/journal.pone.0317519 · Cited by 4 · see citing works

Multi-task learning for aspect level semantic classification combining complex aspect target semantic enhancement and adaptive local focus

Zhu, Q., Wang, X., Liu, X., Du, W., & Ding, X. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023824 · Cited by 4 · see citing works

Hybrid Deep Learning Model to Predict Students’ Sentiments in Higher Educational Institutions

Ananthi Claral Mary, T. (2025) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v19i01.50883 · Cited by 4 · see citing works

X (Twitter) Sentiment Analysis Based on Hybrid Approach: An Application for Online Food Ordering

GÜNEŞ, Y., & Arıkan, M. (2025) · Bilişim Teknolojileri Dergisi · doi:10.17671/gazibtd.1616709 · Cited by 4 · see citing works

The Effectiveness of the Ensemble Naive Bayes in Analyzing Review Sentiment of the Lazada Application on Google Play

Hashim, K. A., Sibaroni, Y., & Prasetyowati, S. S. (2024) · ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) · doi:10.1109/icetsis61505.2024.10459562 · Cited by 4 · see citing works

Online Verbal Aggression on Social Media During Times of Political Turmoil: Discursive Patterns from Poland’s 2020 Protests and Election

Domalewska, D. (2025) · Journalism and Media · doi:10.3390/journalmedia6030146 · Cited by 3 · see citing works

Comparing Optimizer Strategies For Enhancing Emotion Classification In IndoBERT Models

Krisna, J. I. T., Luthfiarta, A., Cahya, L. D., Winarno, S., & Nugraha, A. (2024) · Advance Sustainable Science Engineering and Technology · doi:10.26877/asset.v6i2.18228 · Cited by 3 · see citing works

A Review of Different Machine Learning Techniques for Fake Review Identification

Mane, K., & Dongre, S. (2024) · International Conference on Data Intelligence and Cognitive Informatics (ICDICI) · doi:10.1109/icdici62993.2024.10810890 · Cited by 3 · see citing works

Sentiment Analysis Using Transformer Models: BERT, T5, and GPT

Sokolová, Z., Harahus, M., Sokol, M., Kupcová, E., & Pleva, M. (2025) · International Conference Radioelektronika · doi:10.1109/radioelektronika65656.2025.11008427 · Cited by 3 · see citing works

Taxonomy of Opinion Mining, Approaches and Domain Applications: Future Research Direction

Ujah-Ogbuagu, B. C., Ameen, A. O., Okwueleka, F. N., & Ogbuokiri, B. (2025) · SN Computer Science · doi:10.1007/s42979-025-04141-8 · Cited by 3 · see citing works

Leveraging Learning Analytics to Improve Student Engagement

Çiğdem, H., & Öncü, S. (2023) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-6684-9527-8.ch004 · Cited by 3 · see citing works

AutoBloom: AI-Based Classification of Cognitive Levels in Educational Content

Shaik, T., Tao, X., Li, L., Sun, Y., Li, Y. H. M., Zhang, J., & Dann, C. E. (2023) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat59888.2023.00081 · Cited by 3 · see citing works

Sentiment Analysis of Student Evaluation of Teaching Based on Bi-LSTM Algorithm

Yuan, H., & Hernandez, A. A. (2023) · IEEE International Conference on Advanced Infocomm Technology · doi:10.1109/icait59485.2023.10367267 · Cited by 3 · see citing works

Aspect Level Sentiment Analysis with CNN Bi-LSTM and Attention Mechanism

Lin, J., & Najafabadi, M. K. (2023) · IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science (BCD) · doi:10.1109/bcd57833.2023.10466355 · Cited by 3 · see citing works

Emerging Trends and Challenges in Educational Sentiment Analysis

Sweta, S. (2024) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-981-97-2474-1_5 · Cited by 3 · see citing works

Evidence-driven appraisal of students’ careers using process mining: a case study

Diamantini, C., Genga, L., Mircoli, A., & Potena, D. (2024) · Journal of Intelligent Information Systems · doi:10.1007/s10844-024-00904-6 · Cited by 3 · see citing works

Aspect-based sentiment analysis of open-ended responses in student course evaluation surveys

Alotaibi, R. (2024) · Thermal Science · doi:10.2298/tsci2406037a · Cited by 3 · see citing works

Systematic mapping study of tools to identify emotions and personality traits

Islam, A., Noor, N. F. B. M., & Rahman, S. S. A. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00271-3 · Cited by 3 · see citing works

Breaking classification Algorithms: A Black-Box Adversarial Attack for Data Poisoning

Lourdu Mahimai Doss, P., & Gunasekaran, M. (2023) · International Conference on Innovations in Engineering and Technology (ICIET) · doi:10.1109/iciet57285.2023.10220588 · Cited by 3 · see citing works

An enhanced LSTM-CRF based approach for Knowledge Integration based Sentiment Analysis

Balaram, A., Silparaj, M., Sree, K. L., Kumar, V., Kumar, D. V., & Reddy, K. (2025) · International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) · doi:10.1109/isacc65211.2025.10969332 · Cited by 2 · see citing works

Combining RPA and AI to recognize learners’ emotions in Cyberspace: Experiment at some Vietnamese universities

Chu, V.-H., Pham, X., & Nguyen, T.-S. (2025) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste64638.2025.11092155 · Cited by 2 · see citing works

BOARD #56A: Student Feedback Analysis Using Natural Language Processing (NLP) and Sentiment Analysis

Badhan, S. J., Samsami, R., & Nossoni, G. (2025) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--56002 · Cited by 2 · see citing works

Online Public Opinion Situation Assessment by Deep Sparse Autoencoder and Time-Series Modeling Convolutional Neural Network

Sun, W., Xiao, R., & Li, X. (2025) · IEEE Access · doi:10.1109/access.2025.3602491 · Cited by 2 · see citing works

Classification of Sentiment Analysis and Community Opinion Modeling Topics for Application of ICT in Government Operations

Risal, A. A. N., Fathahillah, F., & Sulaiman, D. R. A. (2023) · International Journal of Environment Engineering and Education · doi:10.55151/ijeedu.v5i1.99 · Cited by 2 · see citing works

Analisis Sentimen Menggunakan Metode Klasifikasi Support Vector Machine (SVM) dan Seleksi Fitur Chi-Square

Hokijuliandy, E., Napitupulu, H., & Firdaniza, F. (2023) · SisInfo Jurnal Sistem Informasi dan Informatika · doi:10.37278/sisinfo.v5i2.670 · Cited by 2 · see citing works

Cold-curing mixtures based on biopolymer lignin complex for casting production in single and small-series conditions

Al-Saraireh, F. M. (2023) · AIMS Materials Science · doi:10.3934/matersci.2023047 · Cited by 2 · see citing works

The Impact on Employability by COVID-19 Pandemic - AI Case Studies

Bandi, V. B., Tao, X., Shaik, T., Yong, J., & Zhang, J. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-7254-8_66 · Cited by 2 · see citing works

The ChatGPT Impact on Education: A Comprehensive Bibliometric Review

Utama, I. D., Sudirman, I. D., Nugraha, D. Y., Kurnianingrum, D., & Karmagatri, M. (2024) · Journal of Higher Education Theory and Practice · doi:10.33423/jhetp.v24i5.6996 · Cited by 3 · see citing works

EDUCATIONAL DATA MINING AND LEARNING ANALYTICS: TEXT GENERATORS USAGE EFFECT ON STUDENTS’ GRADES

Pliuskuvienė, B., Radvilaitė, U., Juodagalvytė, R., Ramanauskaitė, S., & Stefanovič, P. (2024) · New Trends in Computer Sciences · doi:10.3846/ntcs.2024.21318 · Cited by 2 · see citing works

Pipelining Semantic Expansion and Noise Filtering for Sentiment Analysis of Short Documents – CluSent Method

Viegas, F. A. R., Canuto, S., da Cunha, W. L. M., França, C., de Andrade, C. M. V., Fonseca, G., Machado, A. C., da Rocha, L. C. D., & Gonçalves, M. A. (2024) · Journal on Interactive Systems · doi:10.5753/jis.2024.4117 · Cited by 2 · see citing works

Discovering Personal Data Security Issues: Insights from “Have I Been Pwned”

Hiền, T. N. T., Sangsongfa, A., & Amdee, N. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-70906-7_22 · Cited by 2 · see citing works

Fusion Text Representations to Enhance Contextual Meaning in Sentiment Classification

Trisna, K. W., Huang, J., Liang, H., & Dharma, E. M. (2024) · Applied Sciences · doi:10.3390/app142210420 · Cited by 2 · see citing works

NLP-Based Sentiment Analysis for Evaluating Student Feedback in English Language Education

Mayormente, M. D., & Gumpal, B. R. (2025) · International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI) · doi:10.1109/icmsci62561.2025.10894214 · Cited by 2 · see citing works

Data-Driven Policy Making Framework Utilizing TOWS Analysis

Son, Y., Kim, Y. T., & Choi, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3572908 · Cited by 2 · see citing works

Investigating relationships of sentiments, emotions, and performance in professional development K-12 CS teachers

Liu, Y. F., Soh, L., Trainin, G., Nugent, G., & Smith, W. (2024) · Computer Science Education · doi:10.1080/08993408.2023.2298162 · Cited by 2 · see citing works

Deep Learning Model to Improve Teaching-Learning Process using Sentiment Analysis

Chauhan, G. S., Garg, N., Nahta, R., & Meena, Y. K. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10306886 · Cited by 1 · see citing works

Educational Data Mining in European Union – Achievements and Challenges: A Systematic Literature Review

Simionescu, C., Danubianu, M., Gradinaru, B. C. P., & Măciucă, M. S. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150386 · Cited by 1 · see citing works

Work in Progress: Use of Natural Language Processing in the Evaluation of University Satisfaction Level

Oblitas, J., & Pineda-Briseño, A. (2024) · IEEE World Engineering Education Conference (EDUNINE) · doi:10.1109/edunine60625.2024.10500583 · Cited by 1 · see citing works

A Comparison Between Transformers and Foundation Models in Sentiment Analysis of Student Evaluation of Teaching

Vega, I., Valencia, J., Arcos, Á., Navarrete, D., & Baldeon-Calisto, M. (2024) · International Symposium on Digital Forensics and Security (ISDFS) · doi:10.1109/isdfs60797.2024.10527264 · Cited by 1 · see citing works

Aspect Level Sentiment Analysis with CNN Bi-LSTM and Attention Mechanism

Lin, J., & Najafabadi, M. K. (2023) · ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter) · doi:10.1109/snpd-winter57765.2023.10466355 · Cited by 1 · see citing works

What do Computing Interns Discuss Online? An Empirical Analysis of Reddit Posts

Popoola, S., Vollem, A., & Nti, I. K. (2024) · SIGCITE · doi:10.1145/3769694.3771103 · Cited by 2 · see citing works

Sentiment Analysis of MOOC Reviews Based on Knowledge Dependency Tree

Wang, H., & Jiao, J. (2025) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3713073 · Cited by 1 · see citing works

CBLTwitter: Twitter disaster detection analysis using CNN-BiLSTM deep learning methods

Çetıner, H., & Yüksel, H. (2025) · Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi · doi:10.17714/gumusfenbil.1653072 · Cited by 1 · see citing works

Integrating Emotional Intelligence Assessment with Sentiment Analysis Using Explainable Machine Learning for Predicting the Developmental Potential of Children: A Literature Review

Shyamala, B., & Acharya, P. S. (2025) · International Journal of Management Technology and Social Sciences · doi:10.47992/ijmts.2581.6012.0391 · Cited by 1 · see citing works

Assessing Student Engagement: A Machine Learning Approach to Qualitative Analysis of Institutional Effectiveness

Ahmed, A., Hayes, M. J., & Joorabchi, A. (2025) · Future Internet · doi:10.3390/fi17100453 · Cited by 1 · see citing works

Python’s Contribution to Artificial Intelligence in Education: A state-of-the-art review

Papadimitriou, A. (2025) · Journal of Future Artificial Intelligence and Technologies · doi:10.62411/faith.3048-3719-267 · Cited by 1 · see citing works

What Is AI? The Need for AI Literacy in Education

Wilton, L., Brett, C., Ip, S., Engineer, R., & Tassis, A. (2025) · Springer eBooks · doi:10.1007/978-3-031-99804-1_4 · Cited by 2 · see citing works

Sentiment Analysis of Learning Management Systems Using Fine-Tuned BERT

Johan, R. C., Rullyana, G., Ardiansah, A., Oktaviani, F. D., Asmara, L. G., & Ramadhan, S. Y. (2025) · doi:10.1109/icet67257.2025.11290825 · Cited by 1 · see citing works

Natural Language Processing for Pedagogical Insight: A Study of User Reviews in Teacher Training Simulators

Susilana, R., Riyana, C., Rullyana, G., Mulyadi, D., Sutisna, M., & Amelia, D. (2025) · doi:10.1109/icet67257.2025.11290770 · Cited by 1 · see citing works

Does Digitalisation Promote the Servicification of Manufacturing? Firm‐Level Evidence From China

Thangavelu, S. M., Wang, W., Zhang, Y., & Zou, J. (2026) · World Economy · doi:10.1111/twec.70071 · Cited by 1 · see citing works

Unveiling Sentiment Nuances: Comparative Study on Aspect Based Sentiment Analysis Approaches

Goyal, N., & Bansal, R. (2025) · Global AI Summit - International Conference on Artificial Intelligence and Emerging Technology (AI Summit) · doi:10.1109/aisummit66170.2025.11410711 · Cited by 1 · see citing works

Sentiment Mining in E-Learning Using ML ModelsPreprint

Kumar, P., Yadav, G., & Singh, H. O. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5899822

Using Opinion Mining as an educational analytic: An integrated strategy for the analysis of students’ feedback

Misuraca, M., Scepi, G., & Spano, M. (2021) · Studies In Educational Evaluation · doi:10.1016/j.stueduc.2021.100979 · Cited by 48 · see citing works

A Survey on Sentimental Analysis of Student Reviews Using Natural Language Processing (NLP) and Text Mining

Jayasudha, J., & Thilagu, M. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-23233-6_27 · Cited by 14 · see citing works

Literature Survey of Lexicon based method and Machine Learning methods of Sentiment Analysis of Student’s Feedback

Krishnakant P. Adhiya, S. A. P. (2023) · Mathematical Statistician and Engineering Applications

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

Kastrati, Z., Dalipi, F., Imran, A. S., Pireva, K., & Wani, M. A. (2021) · Applied Sciences · doi:10.3390/app11093986 · Cited by 258 · see citing works