Linguistic Analysis of Texts in Philological Research: The Use of Salesforce Einstein Artificial Intelligence

Authors

  • Iryna Strashko

    Mykhailo Drahomanov State University of Ukraine, Kyiv, 02000, Ukraine
  • Inesa Melnyk

    Vasyl Stefanyk Precarpathian National University, Ivano-Frankivsk, 76000, Ukraine
  • Valentyna Kozak

    Kyiv Institute of the National Guard of Ukraine, Kyiv, 03179, Ukraine
  • Nataliia Torchynska

    Khmelnytskyi National University, Khmelnytskyi, 29000, Ukraine；

    Khmelnytskyi Scientific Research Forensic Center of the MIA (Minictry of Internal Affairs) of Ukraine, Khmelnytskyi, 29019, Ukraine
  • Olena Dyiak

    Ukrainian State Drahomanov University, Kyiv, 02000, Ukraine

DOI:

https://doi.org/10.30564/fls.v6i3.6601
Received: 29 March 2024 | Revised: 15 April 2024 | Accepted: 5 May 2024 | Published Online: 10 July 2024

Abstract

Artificial intelligence is beginning to spread to all fields of science, and philology, linguistics and literature are no exception. This intersection of technology and the humanities opens a new era of linguistic and literary analysis. The relevance lies in the fact that artificial intelligence offers innovative ways of understanding and interpreting texts. Linguistic analysis of texts is an important part of philological research, as it helps to uncover the meaning, style, and other elements of language use in texts. This study aims to explore the innovative technology of Salesforce Einstein Artificial Intelligence, which can be used for linguistic text analysis in philological research. Research methods include literature review, correlation analysis, social analysis, impact analysis, syntactic analysis, and factor analysis. The results of the work showed that the system deeply learns the language and learns to recognize and analyze various language features and emotions. The paper examines the technical and ethical aspects of Salesforce Einstein AI. A cross-sectional study allowed us to describe the technological capabilities of the system and to debate the problem of replacing the author with artificial intelligence from an ethical perspective. The authors conclude that linguistic text processing in philology has revolutionized the way authors interact with technology and has become a valuable tool in text analysis. Salesforce Einstein AI has opened up a whole new world of opportunities for the field of philology.

Keywords:

Artificial intelligence; Test processing; Tokenization; Emotion analysis; Ethical issue; Natural language

References

Aliyeva, G.B., 2023. Text linguistics and the use of linguistic data in modern technologies: Prospects for development. Futurity of Social Sciences. 1(2), 18–29. DOI: https://doi.org/10.57125/FS.2023.06.20.02

Assunção, G., et al., 2022. An overview of emotion in artificial intelligence. IEEE Transactions on Artificial Intelligence. 3(6), 867–886. DOI: https://doi.org/10.1109/TAI.2022.3159614

Bajohr, H., 2020. Algorithmic empathy: On two paradigms of digital generative literature and the need for a critique of AI works. Available from: https://doi.org/10.25969/mediarep/18799 (cited 10 May 2024).

Barron, L., 2023. AI and Literature. In AI and Popular Culture. Emerald Publishing Limited: Leeds, DL. pp. 47–87. DOI: https://doi.org/10.1108/978-1-80382-327-020231003

Basha, M.J., Vijayakumar, S., Jayashankari, J., et al., 2023. Advancements in natural language processing for text understanding. E3S Web of Conferences; 27-28 April 2023; Tamil Nadu, India: EDP Sciences. p. 04031. DOI: https://doi.org/10.1051/e3sconf/202339904031

Biró, A., Cuesta-Vargas, A.I., Szilágyi, L., 2023. Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis. Acta Universitatis Sapientiae, Informatica. 15(2), 359–403.

Brinkman, D., Grudin, J., 2023. Learning from a generative AI predecessor–The many motivations for interacting with conversational agents. arXiv. DOI: https://doi.org/10.48550/arXiv.2401.02978

Chakraborty, S., Bedi, A.S., Zhu, S., et al., 2023. On the possibilities of ai-generated text detection. arXiv. DOI: https://doi.org/10.48550/arXiv.2304.04736

Chowdhary, K., Chowdhary, K.R., 2020. Natural language processing. Fundamentals of artificial intelligence. 603-649. DOI: https://link.springer.com/chapter/10.1007/978-81-322-3972-7_19

Chun, J., Elkins, K., 2022. What the rise of AI means for narrative studies: A response to “why computers will never read (or write) literature” by Angus Fletcher. Narrative. 30(1), 104–113. DOI: https://doi.org/10.1353/nar.2022.0005

Crane, G., 2019. Beyond Translation: Language Hacking and Philology. Harvard Data Science Review. 1(2). DOI: https://doi.org/10.1162/99608f92.282ad764

Da, N.Z., 2019. The computational case against computational literary studies. Critical Inquiry. 45(3), 601–639. DOI: https://doi.org/10.1086/702594

Davenport, T.H., 2018. From analytics to artificial intelligence. Journal of Business Analytics. 1(2), 73–80. DOI: https://doi.org/10.1080/2573234x.2018.1543535

Durmishi, L., Durmishi, A., 2022. A philosophical assessment of social networks impact on adolescents’ development in conditions of unlimited access to information. Futurity Philosophy. 1(2), 27–41. DOI: https://doi.org/10.57125/FP.2022.06.30.03

Fenves, P., 2019. “Einstein’s Brain” in Three Parts. The Yearbook of Comparative Literature. 62(1), 174–188. DOI: https://doi.org/10.3138/ycl.62.005

Gervais, D.J., 2021. AI Derivatives: The Application to the Derivative Work Right to Literary and Artistic Productions of AI Machines. Available from: https://heinonline.org/HOL/LandingPage?handle=hein.journals/shlr52&div=37&id=&page= (cited 10 May 2024).

Glikson, E., Woolley, A.W., 2020. Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals. 14(2), 627–660. DOI: https://doi.org/10.5465/annals.2018.0057

Harris, P., Nambiar, R., Rajasekharan, A., et al., 2020. AI Powered Analytics App for Visualizing Accident-Prone Areas. EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing: BDCC 2018; 13–15 December 2018; Coimbatore, India: Springer International Publishing. p. 361–367. DOI: https://doi.org/10.1007/978-3-030-19562-5_36

Heflin, J.J.A., 2020. AI-generated literature and the vectorized Word. Available from: https://dspace.mit.edu/handle/1721.1/127563 (cited 10 May 2024).

Jalilbayli, O.B., 2022. Philosophy of linguistic culture and new perspectives in modern azerbaijani linguistics. Futurity Philosophy. 1(4), 53–65. DOI: https://doi.org/10.57125/FP.2022.12.30.05

Jones, N., 2022. Experiential literature? Comparing the work of AI and human authors. APRIA Journal. 5(5), 41–57. DOI: https://doi.org/10.37198/APRIA.04.05.a5

Kaliuta, K., 2023. Integration of AI for routine tasks using salesforce. Asian Journal of Research in Computer Science. 16(3), 119–127. DOI: https://doi.org/10.9734/ajrcos/2023/v16i3350

Kaliuta, K., 2024. Economic benefits of using salesforce in business: Analysis and practical recommendations. Futurity Economics and Law. 4(2), 83–99. DOI: https://doi.org/10.57125/FEL.2024.06.25.05

Kampen, K., Romanchuk, S., Palij, D., 2022. Confessional style of the Ukrainian language. Occasional Papers on Religion in Eastern Europe. 42(2), 11. DOI: https://doi.org/10.55221/2693-2148.2331

Maraieva, U., 2022. On the formation of a new information worldview of the future (literature review). Futurity Philosophy. 1(1), 18–29. DOI: https://doi.org/10.57125/FP.2022.03.30.02

Nikolenko, K., 2022. Artificial intelligence and society: Pros and cons of the present, future prospects. Futurity Philosophy. 1(2), 54–67. DOI: https://doi.org/10.57125/FP.2022.06.30.05

Pak, A., Hurbanska, S., Boiko, O., et al., 2023. The formalized semantics of neologisms-slangisms in the context of the English translation of A military narrative. World Journal of English Language. 13(6), 537. DOI: https://doi.org/10.5430/wjel.v13n6p537

Parmar, D., 2023. Enhancing customer relationship management with Salesforce Einstein GPT. Available from: https://www.theseus.fi/bitstream/handle/10024/812434/Parmar_Dipal.pdf?sequence=2&isAllowed=y (cited 10 May 2024).

Pelau, C., Dabija, D.-C., Ene, I., 2021. What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry. Computers in Human Behavior. 122, 106855. DOI: https://doi.org/10.1016/j.chb.2021.106855

Salesforce announces Einstein GPT, the world’s first generative AI for CRM. Available from: https://www.salesforce.com/news/press-releases/2023/03/07/einstein-generative-ai/?bc=DB (cited 10 May 2024).

Sengupta, E., Garg, D., Choudhury, T., et al., 2018. Techniques to elimenate human bias in machine learning. 2018 International Conference on System Modeling and Advancement in Research Trends (SMART); 23-24 November 2018; Moradabad, India: IEEE.p. 226–230. DOI: https://doi.org/10.1109/SYSMART.2018.8746946

Shrivastava, M., 2017. Learning Salesforce Einstein. Available from: https://www.packtpub.com/product/learning-salesforce-einstein/9781787126893 (cited 10 May 2024).

Shukla, A., 2022. Utilizing AI and machine learning for human emotional analysis through speech-to-text engine data conversion. Journal of Artificial Intelligence and Cloud Computing. 1(1), 1–4. DOI: https://doi.org/10.47363/jaicc/2022(1)145

Suter, R., 2019. Artificial intelligence and the cloud. Artificial intelligence and machine learning for business for non-engineers (1st ed.). CRC Press: FL. pp.27–28. DOI: https://doi.org/10.1201/9780367821654-3

Tripto, N.I., Uchendu, A., Le, T., et al., 2023. HANSEN: human and AI spoken text benchmark for authorship analysis. arXiv. DOI: https://doi.org/10.48550/arXiv.2310.16746

Van Heerden, I., Bas, A., 2021. Viewpoint: AI as author – bridging the gap between machine learning and literary theory. The Journal of Artificial Intelligence Research. 71, 175–189. DOI: https://doi.org/10.1613/jair.1.12593

Wei, C., 2022. Copyright protection and data reliability of AI-written literary creations in smart city. Security and Communication Networks. 2022, 1–13. DOI: https://doi.org/10.1155/2022/6498468

Yu, J., 2019. Getting started with salesforce Einstein analytics: A beginner’s guide to building interactive dashboards. Apress: NY.

Zhao, G., Li, Y., Xu, Q., 2022. From Emotion AI to Cognitive AI. International Journal of Network Dynamics and Intelligence. 1(1), 65–72. DOI: https://doi.org/10.53941/ijndi0101006

Downloads

How to Cite

Strashko, I., Melnyk, I., Kozak, V., Torchynska, N., & Dyiak, O. (2024). Linguistic Analysis of Texts in Philological Research: The Use of Salesforce Einstein Artificial Intelligence. Forum for Linguistic Studies, 6(3), 247–259. https://doi.org/10.30564/fls.v6i3.6601

Issue

Vol. 6 , Iss. 3 (September 2024): In progress

Article Type

Review

License

Copyright © 2024 Iryna Strashko, Inesa Melnyk, Valentyna Kozak, Nataliia Torchynska, Olena Dyiak

Creative Commons License

This is an open access article under the Creative Commons Attribution 4.0 International License.