CAPABILITIES AND APPLICATION OF ARTIFICIAL INTELLIGENCE (AI) MODELS IN QUALITATIVE AND QUANTITATIVE DATA MINING, DATA PROCESSING AND DATA ANALYSIS

Moises C. Torrentira Jr.

Abstract


The study was conducted to determine the capabilities and application of Artificial Intelligence in the development of themes in qualitative and quantitative data analysis. Multi-stage data mining as a tool in AI data collection was employed by the researcher to process data reduction and the extraction of codes. This technique was able to generate the final themes pertaining to the capabilities and application of AI models, which include pattern recognition analysis, thematic analysis, sentiment analysis, efficient data processing, objectivity and bias reduction, and insight generation. These capabilities and applications of AI models can be used to highlight the benefits and ethical use of AI in research, especially in data mining, data processing, and data analysis.

 

Article visualizations:

Hit counter


Keywords


capabilities and application, artificial intelligence (AI) models, qualitative, quantitative, data mining, data processing, and data analysis

Full Text:

PDF

References


"Artificial intelligence technology behind ChatGPT was built in Iowa — with a lot of water". AP News. September 9, 2023. Archived from the original on September 10, 2023. Retrieved on September 10, 2023.

Asatryan, B., Bleijendaal, H., & Wilde, A. A. (2023). Towards advanced diagnosis and management of inherited arrhythmia syndromes: Harnessing the capabilities of artificial intelligence and machine learning. Heart Rhythm.

Chowdhary, K., & Chowdhary, K. R. (2020). Natural language processing. Fundamentals of artificial intelligence, 603-649. https://doi.org/10.1016/j.hrthm.2023.07.001

Baviskar, D., Ahirrao, S., Potdar, V., & Kotecha, K. (2021). Efficient automated processing of the unstructured documents using artificial intelligence: A systematic literature review and future directions. IEEE Access, 9, 72894-72936. Retrieved from https://ieeexplore.ieee.org/document/9402739

Belotto, M. J. (2018). Data analysis methods for qualitative research: Managing the challenges of coding, interrater reliability, and thematic analysis. The qualitative report, 23(11), 2622-2633. http://dx.doi.org/10.46743/2160-3715/2018.3492

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer 645-678. Retrieved from https://link.springer.com/book/9780387310732

Chakriswaran, P., Vincent, D. R., Srinivasan, K., Sharma, V., Chang, C. Y., & Reina, D. G. (2019). Emotion AI-driven sentiment analysis: A survey, future research directions, and open issues. Applied Sciences, 9(24), 5462. Retrieved from https://www.mdpi.com/2076-3417/9/24/5462

Huang, CJ., Chen, PW. & Pan, WT. Using multi-stage data mining technique to build forecast model for Taiwan stocks. Neural Comput & Applic 21, 2057–2063 (2012). https://doi.org/10.1007/s00521-011-0628-0

Kelly, S., Kaye, S.A., Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, Vol. 77. https://doi.org/10.1016/j.tele.2022.101925

Ma, L., & Sun, B. (2020). Machine learning and AI in marketing–Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481-504. https://doi.org/10.1016/j.ijresmar.2020.04.005

Marshall, D. T., & Naff, D. B. (2024). The Ethics of Using Artificial Intelligence in Qualitative Research. Journal of empirical research on human research ethics: JERHRE, 19(3), 92–102. https://doi.org/10.1177/15562646241262659

Riger, S., & Sigurvinsdottir, R. (2016). Thematic analysis. In L. A. Jason & D. S. Glenwick (Eds.), Handbook of methodological approaches to community-based research: Qualitative, quantitative, and mixed methods (pp. 33–41). Oxford University Press.

Silberg, J., & Manyika, J. (2019). Notes from the AI frontier: Tackling bias in AI (and in humans). McKinsey Global Institute, 1(6), 1-31. Retrieved from https://www.mckinsey.com/~/media/mckinsey/featured%20insights/artificial%20intelligence/tackling%20bias%20in%20artificial%20intelligence%20and%20in%20humans/mgi-tackling-bias-in-ai-june-2019.pdf

Sun, W., Nasraoui, O., & Shafto, P. (2020). Evolution and impact of bias in human and machine learning algorithm interaction. Plos one, 15(8), e0235502. https://doi.org/10.1371/journal.pone.0235502

Taherdoost, H., & Madanchian, M. (2023). Artificial intelligence and sentiment analysis: A review in competitive research. Computers, 12(2), 37. https://doi.org/10.3390/computers12020037

Terry, G., Hayfield, N., Clarke, V., & Braun, V. (2017). Thematic analysis. The SAGE handbook of qualitative research in psychology, 2(17-37), 25.

Turner, S. F., Cardinal, L. B., & Burton, R. M. (2017). Research design for mixed methods: A triangulation-based framework and roadmap. Organizational research methods, 20(2), 243-267. https://doi.org/10.4135/9781526405555

Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q. L., & Tang, Y. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122-1136. Retrieved from https://ieeexplore.ieee.org/document/10113601




DOI: http://dx.doi.org/10.46827/ejes.v11i9.5521

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Moises C. Torrentira Jr.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2015-2023. European Journal of Education Studies (ISSN 2501 - 1111) is a registered trademark of Open Access Publishing Group. All rights reserved.


This journal is a serial publication uniquely identified by an International Standard Serial Number (ISSN) serial number certificate issued by Romanian National Library (Biblioteca Nationala a Romaniei). All the research works are uniquely identified by a CrossRef DOI digital object identifier supplied by indexing and repository platforms. All authors who send their manuscripts to this journal and whose articles are published on this journal retain full copyright of their articles. All the research works published on this journal are meeting the Open Access Publishing requirements and can be freely accessed, shared, modified, distributed and used in educational, commercial and non-commercial purposes under a Creative Commons Attribution 4.0 International License (CC BY 4.0).