Keywords
Opinion mining
Deep learning
Explainable AI
Multilingual NLP
How to Cite
Abstract
Purpose: This paper reviews the current state, progress, and challenges of Sentiment Analysis (SA), a computational approach to identifying and interpreting human opinions and emotions from text. It aims to evaluate the evolution of methods, highlight persistent limitations, and explore emerging directions toward developing generalizable, explainable, and ethically responsible sentiment analysis systems.
Design/Methodology/Approach: The review integrates evidence from two key sources: (1) a tertiary study (Lighart et al., 2021) summarizing outcomes from 14 systematic literature reviews and mapping studies, and (2) a domain-specific review (Sweta, 2024) on sentiment analysis in education. Through thematic analysis, the study identifies methodological trends, core challenges, and innovations shaping the discipline, linking large-scale findings with domain-level applications.
Findings: The analysis reveals a significant methodological transition—from lexicon-based and traditional machine learning models (SVM, Naive Bayes) to deep learning and transformer-based architectures (CNN, LSTM, BERT, GPT). Despite progress, key issues persist: domain and language dependency, lack of contextual sensitivity (sarcasm, irony), data imbalance, model opacity, and privacy concerns. New directions, such as cross-domain and multilingual modeling, explainable AI (XAI), multimodal analysis, and real-time SA, demonstrate promising solutions.
Research Limitations/Implications: The review highlights the need for cross-lingual datasets, explainable architectures, and interdisciplinary collaborations to enhance generalizability, fairness, and ethical compliance.
Originality/Value: By combining high-level synthesis with domain-specific insights, this work provides a comprehensive and forward-looking framework for building robust, transparent, and socially responsible sentiment analysis models.
References
Cambria, E., et al.: SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 105–114 (2020).
Dashtipour, K., et al.: Multilingual Sentiment Analysis: State of the Art and Independent Comparison of Techniques. Cognitive Computation. 8(4), 757–771 (2016). https://doi.org/10.1007/s12559-016-9415-7
Devlin, J., et al.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186 (2019).
Evans, D.: Emotion: The science of sentiment. American Journal of Orthopsychiatry. 72(4), 133–144 (2002).
Fiok, K., Farahani, F.V., Karwowski, W., Ahram, T.: Explainable artificial intelligence for education and training. The Journal of Defense Modeling and Simulation. 19(2), 133–144 (2022). https://doi.org/10.1177/1548512920981595
Hajrizi, R., Nucj, K.P.: Aspect-Based Sentiment Analysis in Education Domain. arXiv preprint arXiv:2010.01429 (2020).
Kaur, G., Gujrati, R., & Uygun, H. (2023). How does AI fit into the management of human resources?. Review of Artificial Intelligence in Education, 4(00), e4. https://doi.org/10.37497/rev.artif.intell.education.v4i00.4
Kasmuri, E., Basiron, H.: Subjectivity analysis in opinion mining—A systematic literature review. International Journal of Advanced Soft Computing Applications. 9(3) (2017).
Liang, W., et al.: Advances, challenges and opportunities in creating data for trustworthy AI. Nature Machine Intelligence. 4(8), 669–677 (2022). https://doi.org/10.1038/s42256-022-00516-1
Lighart, A., Catal, C., Tekinerdogan, B.: Systematic reviews in sentiment analysis: a tertiary study. Artificial Intelligence Review. 54, 4997–5053 (2021). https://doi.org/10.1007/s10462-021-09973-3
Liu, B.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. 5(1), 1–167 (2012).
Poria, S., et al.: Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines. IEEE Intelligent Systems. 33(6), 17–25 (2018). https://doi.org/10.1109/MIS.2018.2880005
Prokhorova, Y., Gujrati, R., & Uygun, H. (2024). The use of AI Chatbots in higher education: the problem of plagiarism. Review of Artificial Intelligence in Education, 5(00), e031. https://doi.org/10.37497/rev.artif.intell.educ.v5i00.31
Qazi, A., et al.: A systematic literature review on opinion types and sentiment analysis techniques: Tasks and challenges. Internet Research. 27(3), 608–630 (2017). https://doi.org/10.1108/IntR-04-2016-0086
Romero, C., Ventura, S.: Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery. 10(3), e1355 (2020). https://doi.org/10.1002/widm.1355
Silbar, B., Zlatic, L., R., N.: Ethical and Privacy Issues of Learning Analytics in Higher Education. In: INTED2021 Proceedings, pp. 7766–7775 (2021).
Sweta, S.: Emerging Trends and Challenges in Educational Sentiment Analysis. In: SpringerBriefs in Applied Sciences and Technology. Springer, Singapore (2024). https://doi.org/10.1007/978-981-87424-1_5
Wang, Y., et al.: Attention-based LSTM for Aspect-level Sentiment Classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606–615 (2016).
Wankhade, M., Rao, A.C.S., Kulkarni, C.: A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review. 55(7), 5731–5780 (2022). https://doi.org/10.1007/s10462-022-10138-z
Zhang, W., et al.: A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges. IEEE Transactions on Knowledge and Data Engineering. 35(11), 11019–11038 (2023). https://doi.org/10.1109/TKDE.2022.3232985
Zhang, Y., Liu, B., Wang, S.: Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 8(4), e1253 (2018). https://doi.org/10.1002/widm.1253
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