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The dynamics of natural language processing and text mining under emerging artificial intelligence techniques

Author

Listed:
  • U. M. Fernandes Dimlo

    (Sreyas Institute of Engineering and Technology)

  • V. Rupesh

    (Department of IT Malla Reddy University)

  • Yeligeti Raju

    (Vignana Bharathi Institute of Technology (Autonomous))

Abstract

In the contemporary era, with the emergence of distributed computing and storage facilities, there has been an increase in the creation of textual data. The invention of the Internet of Things (IoT) and its use cases also led to the creation of big data in textual corpora. At the same time, there are emerging Artificial Intelligence (AI) techniques for processing data in unstructured format. In this context, an important research question is how Natural Language Processing (NLP) and text mining cope with emerging AI techniques. This paper investigates the hypothesis that “NLP and text mining play an increased role in emerging AI techniques.” The investigation uses a dual approach: a literature review and an empirical study. Different aspects of the study, including data science approaches covering AI techniques, are investigated. NLP and text mining are indispensable for meaningful AI outcomes in solving different real-world problems. This paper sheds light on the investigations made and paves the way for exciting future research into utilizing AI along with NLP and text mining. It has covered the research reflecting the dynamics of natural language processing and text mining under emerging artificial intelligence techniques.

Suggested Citation

  • U. M. Fernandes Dimlo & V. Rupesh & Yeligeti Raju, 2024. "The dynamics of natural language processing and text mining under emerging artificial intelligence techniques," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(9), pages 4512-4526, September.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:9:d:10.1007_s13198-024-02468-8
    DOI: 10.1007/s13198-024-02468-8
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    References listed on IDEAS

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    1. Su Jin Choi & So Won Choi & Jong Hyun Kim & Eul-Bum Lee, 2021. "AI and Text-Mining Applications for Analyzing Contractor’s Risk in Invitation to Bid (ITB) and Contracts for Engineering Procurement and Construction (EPC) Projects," Energies, MDPI, vol. 14(15), pages 1-28, July.
    2. Nebojsa Bacanin & Miodrag Zivkovic & Catalin Stoean & Milos Antonijevic & Stefana Janicijevic & Marko Sarac & Ivana Strumberger, 2022. "Application of Natural Language Processing and Machine Learning Boosted with Swarm Intelligence for Spam Email Filtering," Mathematics, MDPI, vol. 10(22), pages 1-31, November.
    3. Xiao Zhou & Lu Huang & Yi Zhang & Miaomiao Yu, 2019. "A hybrid approach to detecting technological recombination based on text mining and patent network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 699-737, November.
    4. Sheth, Jagdish & Kellstadt, Charles H., 2021. "Next frontiers of research in data driven marketing: Will techniques keep up with data tsunami?," Journal of Business Research, Elsevier, vol. 125(C), pages 780-784.
    5. Israel Griol-Barres & Sergio Milla & Antonio Cebrián & Huaan Fan & Jose Millet, 2020. "Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing," Sustainability, MDPI, vol. 12(19), pages 1-22, September.
    6. Venkatesh Shankar & Sohil Parsana, 2022. "An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1324-1350, November.
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