Skip to main content Skip to journal navigation menu Skip to site footer
contact@ukscip.com
Review

Natural Language Processing in Competitive Intelligence: A Systematic Review

Dan Lu ORCID ,  Burin Srisomthawin ORCID ,  Xiawei Chen ORCID ,  Zheyun Zheng ORCID
    Received: 26 March 2026; Revised: 15 April 2026; Accepted: 23 April 2026; Published: 15 October 2026

    Abstract

    This study examines how natural language processing (NLP) supports the competitive intelligence (CI) process and how its integration affects organizational decision-making. The review also identifies major research trends, practical applications, and key gaps in NLP-enabled CI research. A systematic literature review was conducted using the PRISMA approach. Relevant studies were retrieved from Scopus and Web of Science through structured Boolean search strings combining NLP-, CI-, and decision-making-related terms. After duplicate removal, title and abstract screening, full-text eligibility assessment, and quality appraisal, 94 peer-reviewed studies published between 2022 and 2026 were included in the final synthesis. Although NLP has been widely discussed in business analytics and artificial intelligence research, its specific role in CI remains fragmented across disciplines. This review addresses that gap by consolidating evidence from information systems, management, and computational research and by proposing an integrated framework linking NLP techniques, CI stages, decision effects, and risk pathways. The review shows that NLP supports CI through automated information gathering, content summarization, sentiment analysis, trend detection, weak signal identification, competitive landscape mapping, predictive analytics, and insight communication. However, the organizational value of NLP is not determined by technological adoption alone. It depends on source quality, model interpretability, human oversight, and alignment between analytical outputs and managerial needs. The review also identifies emerging practical applications in competitor monitoring, reputation analysis, market sensing, and risk early-warning. This study contributes a framework that explains both value creation and risk propagation in NLP-enabled CI systems. It also outlines concrete directions for future research, including longitudinal outcome studies, CI-specific benchmarks, human–AI collaboration design, hallucination mitigation, and ethical governance mechanisms.

    Keywords

    References

    1. 1. Elkaabi, K.; Mamouny, A.; Elmaallam, M. The impact of artificial intelligence tools on the competitive intelligence process: A systematic literature review. In Proceedings of the 2025 International Conference on Circuit, Systems and Communication (ICCSC), Fez, Morocco, 19–20 June 2025; pp. 1–7. DOI: https://doi.org/10.1109/ICCSC66714.2025.11135245
    2. 2. Punukollu, M. NLP applications in mining pharmaceutical text data for competitive intelligence and market insights. Essex J. AI Ethics Responsible Innov. 2023, 3, 594–635. Available online: https://www.ejaeai.org/index.php/publication/article/view/86
    3. 3. Alafi, K.K.; Ismaeel, B.; Almarshad, M.N.; et al. Analysis of competitive intelligence in retail management in the Jordanian market from the consumer’s perspective. J. Intell. Stud. Bus. 2024, 13, 24–38.
    4. 4. Adil, B.; Abdelhadi, F. A big data analytics framework for competitive intelligence systems. J. Theor. Appl. Inf. Technol. 2022, 100, 149–169.
    5. 5. Loiko, A.I. Technology of digital ecosystems. Vestn. Samar. Gos. Tekh. Univ. Ser. Filos. 2022, 4, 49–56. DOI: https://doi.org/10.17673/vsgtu-phil.2022.1.7
    6. 6. Tsiu, S.V.; Ngobeni, M.; Mathabela, L.; et al. Applications and competitive advantages of data mining and business intelligence in SMEs performance: A systematic review. Businesses 2025, 5, 22. DOI: https://doi.org/10.3390/businesses5020022
    7. 7. Kalyampudi, P.L. Natural language processing for business intelligence and market analysis. In Artificial Intelligence and Machine Learning in Management Science: Emerging Research and Applications; NC Publishers: Karachi, Pakistan, 2025; p. 97.
    8. 8. Taherdoost, H.; Madanchian, M. Artificial intelligence and sentiment analysis: A review in competitive research. Computers 2023, 12, 37. DOI: https://doi.org/10.3390/computers12020037
    9. 9. Olujimi, P.A.; Ade-Ibijola, A. NLP techniques for automating responses to customer queries: A systematic review. Discov. Artif. Intell. 2023, 3, 20. DOI: https://doi.org/10.1007/s44163-023-00065-5
    10. 10. Wiggins, W.F.; Tejani, A.S. On the opportunities and risks of foundation models for natural language processing in radiology. Radiol. Artif. Intell. 2022, 4, e220119.
    11. 11. Hosseini, M.-S.; Jahanshahlou, F.; Akbarzadeh, M.A.; et al. Formulating research questions for evidence-based studies. J. Med. Surg. Public Health 2024, 2, 100046. DOI: https://doi.org/10.1016/j.glmedi.2023.100046
    12. 12. Madureira, L.; Popovič, A.; Castelli, M. Competitive intelligence empirical validation and application: Foundations for knowledge advancement and relevance to practice. J. Inf. Sci. 2023. DOI: https://doi.org/10.1177/01655515231191221
    13. 13. Tyagi, N.; Bhushan, B. Demystifying the role of natural language processing (NLP) in smart city applications: Background, motivation, recent advances, and future research directions. Wireless Pers. Commun. 2023, 130, 857–908. DOI: https://doi.org/10.1007/s11277-023-10312-8
    14. 14. Vysotska, V. Modern state and prospects of information technologies development for natural language content processing. In Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Systems, Lviv, Ukraine, 12–13 April 2024; pp. 198–234.
    15. 15. Adepoju, A.H.; Austin-Gabriel, B.; Eweje, A.; et al. Framework for automating multi-team workflows to maximize operational efficiency and minimize redundant data handling. IRE J. 2022, 5, 663–664.
    16. 16. Somaya, J.; Hasna, E.E.; Abdelwahed, H.E.; et al. Automating information extraction from textual documents using NLP techniques: A review of integration in document management systems. In Proceedings of the 4th International Conference on Advances in Communication Technology and Computer Engineering, Marrakech, Morocco, 29–30 November 2024; pp. 507–518. DOI: https://doi.org/10.1007/978-3-031-94623-3_44
    17. 17. Zhang, Z.; Qin, W.; Xu, H. A visual webpage information extraction framework for competitive intelligence system. Scalable Comput. Pract. Exper. 2024, 25, 4138–4152. DOI: https://doi.org/10.12694/scpe.v25i5.3078
    18. 18. Wang, B.; Ma, K.; Wu, L.; et al. Visual analytics and information extraction of geological content for text-based mineral exploration reports. Ore Geol. Rev. 2022, 144, 104818. DOI: https://doi.org/10.1016/j.oregeorev.2022.104818
    19. 19. Zhang, Q.; Wang, B.; Huang, V.S.; et al. Document parsing unveiled: Techniques, challenges, and prospects for structured information extraction. arXiv preprint 2024, arXiv:2410.21169. DOI: https://doi.org/10.48550/arXiv.2410.21169
    20. 20. Liu, D. Design information extraction and visual representation based on artificial intelligence natural language processing techniques. In Proceedings of the 2023 4th International Conference on Computer Vision, Image and Deep Learning (CVIDL), Zhuhai, China, 12–14 May 2023; pp. 154–158. DOI: https://doi.org/10.1109/CVIDL58838.2023.10165716
    21. 21. Khan, B.; Shah, Z.A.; Usman, M.; et al. Exploring the landscape of automatic text summarization: A comprehensive survey. IEEE Access 2023, 11, 109819–109840. DOI: https://doi.org/10.1109/ACCESS.2023.3322188
    22. 22. Sharma, N.; Singh, S.; Kapoor, M. Context-based review on auto text summarization techniques using natural language processing for performing evaluation and comparative analysis of ATS techniques. SN Comput. Sci. 2026, 7, 15. DOI: https://doi.org/10.1007/s42979-025-04621-x
    23. 23. Ovalle, C. Application for monitoring sentiment analysis. In Proceedings of the 13th Computer Science Online Conference 2024, Zlín, Czech Republic, 25–28 April 2024; p. 340. DOI: https://doi.org/10.1007/978-3-031-70285-3_25
    24. 24. Sheng, L.; Wang, Z.; Zhang, L.; et al. Application of sentiment analysis based on deep learning in public opinion monitoring in healthcare networks. Int. J. High Speed Electron. Syst. 2025. DOI: https://doi.org/10.1142/S0129156425404279
    25. 25. Huang, Z.; Tay, E.; Wee, D.; et al. Public perception of the use of digital contact-tracing tools after the COVID-19 lockdown: Sentiment analysis and opinion mining. JMIR Formative Res. 2022, 6, e33314. Available online: https://formative.jmir.org/2022/3/e33314/
    26. 26. Yue, A.; Mao, C.; Chen, L.; et al. Detecting changes in perceptions towards smart city initiatives on Chinese social media: A text mining and sentiment analysis approach. Buildings 2022, 12, 1182. DOI: https://doi.org/10.3390/buildings12081182
    27. 27. Mohamed, K.; Bayraktar, Ü.A. Analyzing the role of sentiment analysis in public relations: Brand monitoring and crisis management. Int. J. Humanities Soc. Sci. 2022, 9, 116–126.
    28. 28. Bordoloi, M.; Biswas, S.K. Sentiment analysis: A survey on design framework, applications, and future scopes. Artif. Intell. Rev. 2023, 56, 12505–12560. DOI: https://doi.org/10.1007/s10462-023-10442-2
    29. 29. Nayak, S. Leveraging natural language processing and machine learning for sentiment analysis in fintech: Enhancing customer insights and decision-making. Int. J. Appl. Eng. Technol. 2022, 4, 242–260.
    30. 30. Revathy, G.; Alghamdi, S.A.; Alahmari, S.M.; et al. Sentiment analysis using machine learning: Progress in machine intelligence for data science. Sustainable Energy Technol. Assess. 2022, 53, 102557. DOI: https://doi.org/10.1016/j.seta.2022.102557
    31. 31. Oloyede, P. Integrating NLP techniques to analyze transactional data for business insights. SSRN Electron. J. 2025. DOI: https://doi.org/10.2139/ssrn.5467469
    32. 32. Shukla, S.; Singh, J.; Nassa, V.K.; et al. Artificial intelligence-driven deep learning for competitive intelligence to enhance market analysis and strategic positioning. In Proceedings of the 2024 4th Asian Conference on Innovation in Technology (ASIANCON), Pimari Chinchwad, India, 23–25 August 2024; pp. 1–5. DOI: https://doi.org/10.1109/ASIANCON62057.2024.10838208
    33. 33. Lobanova, P.A.; Kuzminov, I.F.; Karatetskaia, E.Y.; et al. Trend detection using NLP as a mechanism of decision support. Sci. Tech. Inf. Process. 2023, 50, 440–448. DOI: https://doi.org/10.3103/S0147688223050106
    34. 34. Carvalho, D.; Picoto, W.; Busch, P. Organizational experience of social media: Impacts on competitive intelligence. VINE J. Inf. Knowledge Manage. Syst. 2022, 52, 161–183. DOI: https://doi.org/10.1108/VJIKMS-05-2019-0067
    35. 35. Novikova, E. Influence of NLP on Organizational Design and Development of Dynamic Capabilities. Master's Thesis, Politecnico di Milano, Milan, Italy, 2023. Available online: https://hdl.handle.net/10589/217941
    36. 36. Hassani, A.; Mosconi, E. Social media analytics, competitive intelligence, and dynamic capabilities in manufacturing SMEs. Technol. Forecasting Soc. Change 2022, 175, 121416. DOI: https://doi.org/10.1016/j.techfore.2021.121416
    37. 37. Yuan, H.; Deng, W.; Ma, B.; et al. Monitoring events of market competitors: A text mining method for analyzing massive firm-generated social media. J. Theor. Appl. Electron. Commerce Res. 2023, 18, 908–927. DOI: https://doi.org/10.3390/jtaer18020047
    38. 38. Kim, W.H.; Park, E.; Kim, S.B. Understanding the role of firm-generated content by hotel segment: The case of Twitter. Current Issues Tourism 2023, 26, 122–136. DOI: https://doi.org/10.1080/13683500.2021.2003759
    39. 39. Ju, X. A social media competitive intelligence framework for brand topic identification and customer engagement prediction. PLoS ONE 2024, 19, e0313191. DOI: https://doi.org/10.1371/journal.pone.0313191
    40. 40. Zhao, D.; Tang, Z.; He, D. A systematic literature review of weak signal identification and evolution for corporate foresight. Kybernetes 2024, 53, 3160–3188. DOI: https://doi.org/10.1108/K-03-2023-0343
    41. 41. Ebadi, A.; Auger, A.; Gauthier, Y. Detecting emerging technologies and their evolution using deep learning and weak signal analysis. J. Informetr. 2022, 16, 101344. DOI: https://doi.org/10.1016/j.joi.2022.101344
    42. 42. Albarrak, M.; Salonitis, K.; Jagtap, S. Natural language processing (NLP)-based frameworks for cyber threat intelligence and early prediction of cyberattacks in Industry 4.0: A systematic literature review. Appl. Sci. 2026, 16, 619. DOI: https://doi.org/10.3390/app16020619
    43. 43. Griol Barres, I. Modelling of a System for the Detection of Weak Signals through Text Mining And NLP: Proposal of Improvement by a Quantum Variational Circuit. PhD Thesis, Universitat Politècnica de València, Valencia, Spain, 2022. Available online: https://riunet.upv.es/server/api/core/bitstreams/68f241b7-e4c7-4697-b100-6f4ce8fe4d29/content
    44. 44. Lopez-Pujalte, C.; Tena-Mateos, M.J.; Muñoz-Cañavate, A. A technology watch/competitive intelligence–based decision-support system optimised with genetic algorithms. J. Inf. Sci. 2022, 51. DOI: https://doi.org/10.1177/01655515221133531
    45. 45. Sharma, R.; Agarwal, P.; Arya, A. Natural language processing and big data: A strapping combination. In New Trends and Applications in Internet of Things (IoT) and Big Data Analytics; Springer: Cham, Switzerland, 2022; pp. 255–271. Available online: https://link.springer.com/chapter/10.1007/978-3-030-99329-0_16
    46. 46. Dash, B. Information Extraction from Unstructured Big Data: A Case Study of Deep Natural Language Processing in Fintech. PhD Thesis, University of the Cumberlands, Williamsburg, KY, USA, 2022. Available online: https://www.proquest.com/openview/0a7fccfdbd798ac514d956893df11810
    47. 47. Taha, K. Big data analytics in IoT, social media, NLP, and information security: Trends, challenges, and applications. J. Big Data 2025, 12, 150. DOI: https://doi.org/10.1186/s40537-025-01192-9
    48. 48. Rane, N.L.; Paramesha, M.; Choudhary, S.P.; et al. Machine learning and deep learning for big data analytics: A review of methods and applications. Partners Universal Int. Innov. J. 2024, 2, 172–197. DOI: https://doi.org/10.5281/zenodo.12271006
    49. 49. Chen, Y.; Wei, G.; Lee, E.T. Sentiment analysis and risk early-warning system for cross-border M&A based on natural language processing. Int. J. Comput. Vision Robotics 2025, 15, 1–22. DOI: https://doi.org/10.1504/IJCVR.2025.150869
    50. 50. Fishekqiu, N.S. Exploring the impact of social media sentiment on market dynamics and economic growth: A big data and natural language processing perspective. In Proceedings of the 8th International Scientific Conference on Business and Economics, Tetovo, North Macedonia, 8–9 May 2025; pp. 343–359. DOI: https://doi.org/10.1007/978-3-032-04214-9_22
    51. 51. Silva, D.; Bacao, F. Mapintel: Enhancing competitive intelligence acquisition through embeddings and visual analytics. In Proceedings of the 21st EPIA Conference on Artificial Intelligence, Lisbon, Portugal, 31 August–2 September 2022; pp. 599–610. DOI: https://doi.org/10.1007/978-3-031-16474-3_49
    52. 52. Qian, Y.; Ling, H.; Meng, X.; et al. Voice of the professional: Acquiring competitive intelligence from large-scale professional generated contents. J. Bus. Res. 2024, 180, 114719. DOI: https://doi.org/10.1016/j.jbusres.2024.114719
    53. 53. Wu, J.; Gan, W.; Chen, Z.; et al. AI-generated content (AIGC): A survey. arXiv preprint 2023, arXiv:2304.06632. DOI: https://doi.org/10.48550/arXiv.2304.06632
    54. 54. Zhang, C.; Zhang, C.; Zheng, S.; et al. A complete survey on generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 all you need? arXiv preprint 2023, arXiv:2303.11717. DOI: https://doi.org/10.48550/arXiv.2303.11717
    55. 55. Lin, J.; Jiang, X.; Li, Q.; et al. A competitive intelligence acquisition framework for mining user perception from user-generated content. Appl. Soft Comput. 2023, 147, 110764. DOI: https://doi.org/10.1016/j.asoc.2023.110764
    56. 56. Adewale, G.T.; Victor, A.U.; Sylvia, A.E.; et al. Integrating big data and machine learning in management information systems for predictive analytics: A focus on data preprocessing and technological advancements. World J. Adv. Res. Rev. 2024, 24, 774–789. DOI: https://doi.org/10.30574/wjarr.2024.24.2.3427
    57. 57. Faheem, M.; Awais, M.; Iqbal, A.; et al. Enhancing IT incident management with natural language processing and predictive analytics. Int. J. Sci. Res. Archive 2025, 15, 224–237. DOI: https://doi.org/10.30574/ijsra.2025.15.3.1718
    58. 58. Raeesi Vanani, I.; Taghavifard, M.T.; Yalpanian, M.A. Predictive analytics solution for digital capabilities identification towards business performance improvement. SN Comput. Sci. 2025, 6, 85. DOI: https://doi.org/10.1007/s42979-024-03630-6
    59. 59. Bharadiya, J.P. The role of machine learning in transforming business intelligence. Int. J. Comput. Artif. Intell. 2023, 4, 16–24. DOI: https://doi.org/10.33545/27076571.2023.v4.i1a.60
    60. 60. Yu, Q.; Xing, C.; He, Y.; et al. Intelligent information processing for corporate performance prediction: A hybrid natural language processing (NLP) and deep learning approach. Electronics 2026, 15, 443. DOI: https://doi.org/10.3390/electronics15020443
    61. 61. Shen, L.; Shen, E.; Luo, Y.; et al. Towards natural language interfaces for data visualization: A survey. IEEE Trans. Visual. Comput. Graphics 2023, 29, 3121–3144. DOI: https://doi.org/10.1109/TVCG.2022.3148007
    62. 62. Uddin, M.K.S. A review of utilizing natural language processing and AI for advanced data visualization in real-time analytics. Int. J. Manag. Inf. Syst. Data Sci. 2024, 1, 34–49. DOI: https://doi.org/10.62304/ijmisds.v1i04.185
    63. 63. Islam, M.R.; Razzak, I.; Wang, X.; et al. Natural language interactions enhanced by data visualization to explore insurance claims and manage risk. Ann. Oper. Res. 2024, 339, 1569–1587. DOI: https://doi.org/10.1007/s10479-021-04465-7
    64. 64. Kavaz, E.; Puig, A.; Rodríguez, I. Chatbot-based natural language interfaces for data visualisation: A scoping review. Appl. Sci. 2023, 13, 7025. DOI: https://doi.org/10.3390/app13127025
    65. 65. Agarwal, N.S.; Sonbhadra, S.K. A review on large language models for visual analytics. arXiv preprint 2025, arXiv:2503.15176. DOI: https://doi.org/10.48550/arXiv.2503.15176
    66. 66. Perren, L. Visualising Textual Connections with AI-Driven Insights. Master’s Thesis, Lucerne School of Art and Design, Emmenbrücke, Switzerland, 2023. Available online: https://junior-research.ch/images/database-files/23HS_MADI_Perren_Luiz_thesis_final.pdf
    67. 67. Zhang, H.; Zhang, C.; Wang, Y. Revealing the technology development of natural language processing: A scientific entity-centric perspective. Inf. Process. Manage. 2024, 61, 103574. DOI: https://doi.org/10.1016/j.ipm.2023.103574
    68. 68. Ojadi, J.O.; Onukwulu, E.C.; Somtochukwu, C.; et al. Natural language processing for climate change policy analysis and public sentiment prediction: A data-driven approach to sustainable decision-making. Iconic Res. Eng. J. 2023, 7, 732–751.
    69. 69. Cano-Marin, E.; Sánchez-Alonso, S.; Mora-Cantallops, M. Unleashing competitive intelligence: News mining analysis on technology trends and digital health driving healthcare innovation. IEEE Trans. Eng. Manage. 2023, 71, 12311–12325. DOI: https://doi.org/10.1109/TEM.2023.3326233
    70. 70. Rawas, S.; Tafran, C.; AlSaeed, D.; et al. Transforming healthcare: AI–NLP fusion framework for precision decision-making and personalized care optimization in the era of IoMT. Comput. Mater. Contin. 2024, 81, 4575. DOI: https://doi.org/10.32604/cmc.2024.055307
    71. 71. Abdalrazzaq, A.S.; Meftin, N.K.; Al Fawadi, D.M.; et al. Optimizing robotic interaction and decision making using advanced NLP and AI technologies. In Proceedings of the 2024 36th Conference of Open Innovations Association (FRUCT), Lappeenranta, Finland, 30 October 2024–1 November 2024; pp. 692–701. DOI: https://doi.org/10.23919/FRUCT64283.2024.10749908
    72. 72. Baliga, S.; Sadhale, A.; Upadhyaya, D. Future prospects of natural language processing. In High-Performance Automation Methods for Computational Intelligent Systems; CRC Press: Boca Raton, FL, USA, 2025; pp. 252–278. DOI: https://doi.org/10.1201/9781003559917-10
    73. 73. Chatterjee, S.; Chaudhuri, R.; Mikalef, P. Examining the dimensions of adopting natural language processing and big data analytics applications in firms. IEEE Trans. Eng. Manage. 2022, 71, 3001–3015. DOI: https://doi.org/10.1109/TEM.2022.3202871
    74. 74. Pan, S.K.; Tiwari, A. Improving organizational knowledge control with the use of NLP. In Proceedings of the 2025 International Conference on Intelligent Control, Computing and Communications (IC3), Mathura, India, 13–14 February 2025; pp. 985–991. DOI: https://doi.org/10.1109/IC363308.2025.10956187
    75. 75. Goswami, D.; Rainy, T. Mechanisms by which AI-enabled CRM systems influence customer retention and overall business performance: A systematic literature review of empirical findings. ASRC Procedia: Global Perspect. Sci. Scholarship 2025, 1. DOI: https://doi.org/10.2139/ssrn.5317471
    76. 76. Rzig, D.E.; Hassan, F.; Bansal, C.; et al. Characterizing the usage of CI tools in ML projects. In Proceedings of the 16th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), Helsinki, Finland, 19–23 September 2022; pp. 69–79. DOI: https://doi.org/10.1145/3544902.3546237
    77. 77. Rameesa, K.; Veeramanju, K.T. Analysis of software industry: Natural language processing approach. Focus 2023, 13, 743–752.
    78. 78. Myllynen, T.; Kamau, E.; Mustapha, S.D.; et al. Review of advances in AI-powered monitoring and diagnostics for CI/CD pipelines. Int. J. Multidisciplinary Res. Growth Evaluation 2024, 5, 1119–1130.
    79. 79. Malik, P.; Mittal, V.; Nautiyal, L.; et al. NLP techniques, tools, and algorithms for data science. In Artificial Intelligence for Signal Processing and Wireless Communication; De Gruyter Brill: Berlin, Germany, 2022; p. 123.
    80. 80. Aleqabie, H.J.; Sfoq, M.S.; Albeer, R.A.; et al. A review of text mining techniques: Trends and applications in various domains. Iraqi J. Comput. Sci. Math. 2024, 5, 9. DOI: https://doi.org/10.52866/ijcsm.2024.05.01.009
    81. 81. Lan, M. Natural language processing and text mining. In Artificial Intelligence for Drug Design; Springer: Singapore, 2026; pp. 189–215.
    82. 82. Thakur, K.; Kumar, V. Application of text mining techniques on scholarly research articles: Methods and tools. New Rev. Acad. Librarianship 2022, 28, 279–302. DOI: https://doi.org/10.1080/13614533.2021.1918190
    83. 83. Jabbar, A.; Iqbal, S.; Tamimy, M.I.; et al. An analytical analysis of text stemming methodologies in information retrieval and natural language processing systems. IEEE Access 2023, 11, 133681–133702. DOI: https://doi.org/10.1109/ACCESS.2023.3332710
    84. 84. Shaik, T.; Tao, X.; Dann, C.; et al. Sentiment analysis and opinion mining on educational data: A survey. Nat. Lang. Process. J. 2023, 2, 100003. DOI: https://doi.org/10.1016/j.nlp.2022.100003
    85. 85. Araujo, A.F.; Gôlo, M.P.; Marcacini, R.M. Opinion mining for app reviews: An analysis of textual representation and predictive models. Automated Software Eng. 2022, 29, 5. DOI: https://doi.org/10.1007/s10515-021-00301-1
    86. 86. Bharadwaj, L. Sentiment analysis in online product reviews: Mining customer opinions for sentiment classification. Int. J. Multidisciplinary Res. 2023, 5.
    87. 87. Sarkar, S. A comparative study of different natural language processing techniques for sentiment analysis and opinion mining. Iconic Res. Eng. J. 2025, 8.
    88. 88. Chu, K.E.; Keikhosrokiani, P.; Asl, M.P. A topic modeling and sentiment analysis model for detection and visualization of themes in literary texts. Pertanika J. Sci. Technol. 2022, 30, 2535–2561. DOI: https://doi.org/10.47836/pjst.30.4.14
    89. 89. Yu, D.; Xiang, B. Discovering topics and trends in the field of artificial intelligence using LDA topic modeling. Expert Syst. Appl. 2023, 225, 120114. DOI: https://doi.org/10.1016/j.eswa.2023.120114
    90. 90. Mustaqim, I.Z.; Suryono, R.R. A systematic literature review of topic modeling techniques in user reviews. J. Inf. Syst. Eng. Bus. Intell. 2025, 11, 238–253.
    91. 91. Jehangir, B.; Radhakrishnan, S.; Agarwal, R. A survey on named entity recognition—Datasets, tools, and methodologies. Nat. Lang. Process. J. 2023, 3, 100017. DOI: https://doi.org/10.1016/j.nlp.2023.100017
    92. 92. Sharma, A.; Amrita; Chakraborty, S.; et al. Named entity recognition in natural language processing: A systematic review. In Proceedings of the Second Doctoral Symposium on Computational Intelligence, Lucknow, India, 6 March 2021; pp. 817–828.
    93. 93. Baigang, M.; Yi, F. A review: Development of named entity recognition (NER) technology for aeronautical information intelligence. Artif. Intell. Rev. 2023, 56, 1515–1542. DOI: https://doi.org/10.1007/s10462-022-10197-2
    94. 94. Lagutina, N.S.; Vasilyev, A.M.; Zafievsky, D.D. Tasks in named entity recognition: Technologies and tools. Autom. Control Comput. Sci. 2024, 58, 779–796. DOI: https://doi.org/10.3103/S0146411624700251
    ×