DOI : https://doi.org/10.62527/comien.2.1.22

An Intensive Review on Opinion Mining Techniques: State of The Art, Open Cutting-Edge Challenges, and Future Research Opportunities

Gangfa Ezekiel Magaji (1), Christopher Ifeanyi Eke (2), Damap Tobias Yunana (3)
(1) Department of Computer Science, Faculty of Computing, Federal University, Lafia, Nigeria
(2) Department of Information Technology, Faculty of Computing, Federal University of Lafia, Nigeria
(3) Department of Computer Science, Faculty of Computing, Federal University, Lafia, Nigeria
Fulltext View | Download
How to cite (COMIEN) :
Magaji , G. E., Eke, C. I., & Yunana, D. T. (2025). An Intensive Review on Opinion Mining Techniques: State of The Art, Open Cutting-Edge Challenges, and Future Research Opportunities. International Journal on Computational Engineering, 2(1), 24–30. https://doi.org/10.62527/comien.2.1.22
Citation Format :

Opinion mining (OM), a subfield of natural language processing, has gained significant attention in recent years due to its potential to uncover valuable insights from vast amounts of text data. This paper provides an intensive review of the state-of-the-art methodologies in opinion mining, highlighting their strengths and limitations. We discuss various taxonomy, including Machine Learning Technique, Reinforcement Technique, Transfer Learning Technique, Hybrid Technique, and their applications in sentiment analysis.  This paper also looked at opinion classification, classification process including feature engineering, model construction and confusion matrix as well as evaluation metrics such as accuracy, precision, F1-core, recall and AUC approximation formulae of ROC curve. Our review also identifies various domain application of opinion mining including government and politics, as well as open cutting-edge challenges, such as context and handling of ambiguity, techniques integration and handling of tools, scalability as well as identification and dealing with phrases. Furthermore, we outline future research opportunities, including Explainable AI (EAI) and beyond sentiment for national security. This review aims to provide a valuable resource for scholars and academic practitioners, helping to advance the field of opinion mining and its applications in various domains without restrictions.

C. I. Eke, A. Anir, and N. Liyana, “Sarcasm identification in textual data: Systematic review, research challenges and open directions,” Artif. Intell. Rev., vol. 53, no. 6, pp. 4335–4385, 2019, doi:10.1007/s10462-019-09791-8.

Z. Zhao, W. Liu, and K. Wang, “Research on sentiment analysis method of opinion mining based on multi-model fusion transfer learning,” J. Big Data, vol. 10, no. 1, p. 68, 2023, doi: 10.1186/s40537-023-00837-x.

N. Afiza, M. Razali, N. A. Malizan, N. A. Hasbullah, and M. Wook, “Opinion mining for national security: Techniques, domain applications, challenges and research opportunities,” J. Big Data, vol. 8, no. 1, p. 138, 2021, doi: 10.1186/s40537-021-00536-5.

R. Arifiansyah, “Sentiment analysis in business management: A systematic review,” Ilomata Int. J. Manag., vol. 5, no. 1, pp. 133–156, 2024.

L. P. Hung and S. Alias, “Beyond sentiment analysis: A review of recent trends in text-based sentiment analysis and emotion detection,” J. Adv. Comput. Intell. Intell. Inform., vol. 27, no. 1, pp. 84–94, 2023, doi: 10.20965/jaciii.2023.p0084.

P. Ingale, “A comprehensive survey of sentiment analysis: Techniques, challenges, and applications,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 5, pp. 6496–6505, 2023.

A. Abdullah, Q. Aqlan, B. Manjula, and R. L. Naik, “A study of sentiment analysis: Concepts, techniques, and challenges,” in Proc. Int. Conf. Comput. Intell. Data Sci., Singapore: Springer, 2020, pp. 1–12, doi: 10.1007/978-981-13-6459-4_1.

Q. Ariel, V. Chang, and C. Jayne, “A systematic review of social media-based sentiment analysis: Emerging trends and challenges,” Decis. Anal. J., vol. 3, p. 100073, 2022, doi: 10.1016/j.dajour.2022.100073.

A. Ligthart, C. Catal, and B. Tekinerdogan, “Systematic reviews in sentiment analysis: A tertiary study,” Artif. Intell. Rev., vol. 54, no. 7, pp. 4997–5053, 2021, doi: 10.1007/s10462-021-09973-3.

P. Rita, N. António, A. Patrícia, and P. Rita, “Social media discourse and voting decisions influence: Sentiment analysis in tweets during an electoral period,” Soc. Netw. Anal. Min., vol. 13, no. 1, p. 95, 2023, doi: 10.1007/s13278-023-01048-1.

A. A. Iorkaa and M. Barma, “Machine learning techniques, methods and algorithms: Conceptual and practical insights,” IOSR J. Comput. Eng., vol. 20, no. 8, pp. 55–64, 2021, doi: 10.9790/9622-1108025564.

V. Singh, S. Chen, M. Singhania, B. Nanavati, and A. Gupta, “How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries – A review and research agenda,” Int. J. Inf. Manag. Data Insights, vol. 2, no. 2, p. 100094, 2022, doi: 10.1016/j.jjimei.2022.100094.

A. Hosna, E. Merry, J. Gyalmo, Z. Alom, Z. Aung, and M. A. Azim, “Transfer learning: A friendly introduction,” J. Artif. Intell. Mach. Learn., vol. 4, no. 2, pp. 45–60, 2022.

H. H. Hamid, S. Bangare, A. Nirmal, and P. S. Bangal, “Sentiment analysis: A comprehensive review of techniques and applications,” Int. J. Eng. Res. Technol., vol. 10, no. 8, pp. 775–779, 2023.

A. C. Ojha, P. K. Shah, S. Gupta, and S. Sharma, “Classifying Twitter sentiment on multi-levels using a hybrid machine learning model,” Intell. Syst. Appl. Eng., vol. 12, pp. 328–333, 2024.

P. A. Grana, “Sentiment analysis of text using machine learning models,” Int. J. Comput. Sci. Eng., vol. 10, no. 5, pp. 2952–2956, 2022.

C. I. Eke, A. A. Norman, and L. Shuib, “Context-based feature technique for sarcasm identification in benchmark datasets using deep learning and BERT model,” IEEE Access, vol. 9, pp. 123456–123467, 2021.

A. Patil, “A review on sentiment analysis approaches,” J. Data Anal. Inf. Process., vol. 9, no. 2, pp. 45–60, 2021.

K. K. Mujtaba, L. Shuib, N. Idris, and W. L. Hoo, “Clinical text classification research trends: Systematic literature review and open issues,” J. Med. Syst., vol. 42, no. 1, pp. 21–33, 2018, doi: 10.33094/7.2017.2018.31.21.33.

N. Varghese, B. E. Grace, and M. Kanaga, “Sentiment analysis in social media data for depression detection using artificial intelligence: A review,” SN Comput. Sci., vol. 3, no. 1, p. 74, 2022, doi: 10.1007/s42979-021-00958-1.

M. Wankhade, A. Chandra, S. Rao, and C. Kulkarni, “A survey on sentiment analysis: Challenges and applications,” Artif. Intell. Rev., vol. 55, no. 7, pp. 5735–5780, 2022, doi: 10.1007/s10462-022-10144-1.

B. Klein, Machine Learning with Python Tutorial. O’Reilly Media, 2021.

V. Umarani, A. Julian, and J. Deepa, “Sentiment analysis using various machine learning and deep learning techniques,” J. Data Sci., vol. 3, pp. 385–394, 2021.

M. Z. Ansari, M. B. Aziz, M. O. Siddiqui, H. Mehra, and K. P. Singh, “Analysis of political sentiment orientations on Twitter,” Procedia Comput. Sci., vol. 167, pp. 1821–1828, 2020, doi: 10.1016/j.procs.2020.03.201.

A. Ullah, S. N. Khan, and N. M. Nawi, “Review on sentiment analysis for text classification techniques from 2010 to 2021,” Multimed. Tools Appl., vol. 82, no. 5, pp. 6789–6824, 2023, doi: 10.1007/s11042-022-14112-3.

Z. Karimi, Confusion Matrix: A Practical Guide for Machine Learning Practitioners. Springer, 2021.

A. Poojary, S. Kapoor, and M. Studies, “A review of natural language processing techniques,” J. Nat. Lang. Process., vol. 8, no. 1, pp. 1–16, 2023.

G. L. Kumar, I. Karan, R. Pati, and S. Koduru, “Sentiment analysis in transportation system,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 11, pp. 618–627, 2021.

V. Gopalakrishnan and C. Ramaswamy, “Patient opinion mining to analyze drugs satisfaction using supervised learning,” J. Med. Syst., vol. 41, no. 15, pp. 311–319, 2017.

H. Binali, V. Potdar, and C. Wu, “A state of the art opinion mining and its application domains,” in Proc. IEEE Int. Conf. Ind. Technol., 2014, pp. 1–6, doi: 10.1109/ICIT.2009.4939640.