Positive and Negative Sentiment of Artificial Intelligence in Language: Study Prompt on Chat GPT

Authors

  • M Wildan Universitas Pamulang

DOI:

https://doi.org/10.32493/ljlal.v6i2.40276

Keywords:

Artificial Intelligence, Chat GPT, prompt, sentiment negative, sentiment positive

Abstract

The study explored both positive and negative sentiments towards artificial intelligence in language, focusing on study prompts using Chat GPT. The goal of this study is to understand how users perceive and judge AI in the context of language models such as Chat GPT. The research method used is qualitative descriptive, which focuses on prompts aimed at bringing up language facts. Data collection is carried out through a prompt submission which is then responded to by Chat GPT. Data analysis was carried out using a narrative analysis approach. It is intended to identify patterns of positive and negative sentiment in the responses given by Chat GPT. The results of the study showed that there was a Chat GPT response that led to positive and negative sentiment. The prompts "mohon" and "apakah" are some of the prompts that contain positive elements. The absence of these two prompts is an element in Chat GPT that leads to negative sentiment. The implications of this research are to provide insights for AI developers to improve the quality and acceptance of AI technologies in the future, as well as address existing concerns regarding privacy and accuracy.

References

Aiumtrakul, N., Thongprayoon, C., Arayangkool, C., Vo, K. B., Wannaphut, C., Suppadungsuk, S., Krisanapan, P., Garcia Valencia, O. A., Qureshi, F., Miao, J., & Cheungpasitporn, W. (2024). Personalized Medicine in Urolithiasis: AI Chatbot-Assisted Dietary Management of Oxalate for Kidney Stone Prevention. Journal of Personalized Medicine, 14(1). https://doi.org/10.3390/jpm14010107

Benke, I., Knierim, M. T., & Maedche, A. (2020). Chatbot-based Emotion Management for Distributed Teams: A Participatory Design Study. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2). https://doi.org/10.1145/3415189

Bilquise, G., Ibrahim, S., & Shaalan, K. (2022). Emotionally Intelligent Chatbots: A Systematic Literature Review. Human Behavior and Emerging Technologies, 2022. https://doi.org/10.1155/2022/9601630

Chen, J. S., Le, T. T. Y., & Florence, D. (2021). Usability and responsiveness of artificial intelligence chatbot on online customer experience in e-retailing. International Journal of Retail and Distribution Management, 49(11), 1512–1531. https://doi.org/10.1108/IJRDM-08-2020-0312

Chen, K., Shao, A., Burapacheep, J., & Li, Y. (2024). Conversational AI and equity through assessing GPT-3’s communication with diverse social groups on contentious topics. Scientific Reports, 14(1), 1–12. https://doi.org/10.1038/s41598-024-51969-w

Clocksin, W. F. (2003). Artificial intelligence and the future. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 361(1809), 1721–1748.

Cloninger, C. R. (2006). The science of well-being: an integrated approach to mental health and its disorders. World Psychiatry, 5(2), 71.

Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00392-8

Dubinski, D., Won, S. Y., Trnovec, S., Behmanesh, B., Baumgarten, P., Dinc, N., Konczalla, J., Chan, A., Bernstock, J. D., Freiman, T. M., & Gessler, F. (2024). Leveraging artificial intelligence in neurosurgery—unveiling ChatGPT for neurosurgical discharge summaries and operative reports. Acta Neurochirurgica, 166(1). https://doi.org/10.1007/s00701-024-05908-3

E, K., S, P., R, G., R, K. L., A, B., M, G., T, O., S, R., V, R., H, M., & G, S. (2023). Advantages and pitfalls in utilizing artificial intelligence for crafting medical examinations: a medical education pilot study with GPT-4. BMC Medical Education, 23(1), 772. https://doi.org/10.1186/s12909-023-04752-w

Gkinko, L., & Elbanna, A. (2022). Hope, tolerance and empathy: employees’ emotions when using an AI-enabled chatbot in a digitalised workplace. Information Technology and People, 35(6), 1714–1743. https://doi.org/10.1108/ITP-04-2021-0328

Gual-Montolio, P., Jaén, I., Martínez-Borba, V., Castilla, D., & Suso-Ribera, C. (2022). Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real-or Close to Real-Time: A Systematic Review. International Journal of Environmental Research and Public Health, 19(13). https://doi.org/10.3390/ijerph19137737

Huang, M.-H., & Rust, R. T. (2021). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 30–41.

Jarrahi, M. H. (2019). In the age of the smart artificial intelligence: AI’s dual capacities for automating and informating work. Business Information Review, 36(4), 178–187.

Jin, W., Cheng, Y., Shen, Y., Chen, W., & Ren, X. (2022). A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 1, 2763–2775. https://doi.org/10.18653/v1/2022.acl-long.197

Lawrance, E., Thompson, R., Fontana, G., & Jennings, N. (2021). The impact of climate change on mental health and emotional wellbeing: current evidence and implications for policy and practice. Grantham Institute Briefing Paper, 36, 1–36.

Lee, C. T., Pan, L. Y., & Hsieh, S. H. (2022). Artificial intelligent chatbots as brand promoters: a two-stage structural equation modeling-artificial neural network approach. Internet Research, 32(4), 1329–1356. https://doi.org/10.1108/INTR-01-2021-0030

Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. ACM Computing Surveys, 55(9). https://doi.org/10.1145/3560815

Owoc, M. L., Sawicka, A., & Weichbroth, P. (2019). Artificial intelligence technologies in education: benefits, challenges and strategies of implementation. IFIP International Workshop on Artificial Intelligence for Knowledge Management, 37–58.

Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199–3226. https://doi.org/10.1108/IJCHM-04-2020-0259

Plata, S., De Guzman, M. A., & Quesada, A. (2023). Emerging Research and Policy Themes on Academic Integrity in the Age of Chat GPT and Generative AI. Asian Journal of University Education, 19(4), 743–758. https://doi.org/10.24191/ajue.v19i4.24697

Rapp, A., Curti, L., & Boldi, A. (2021). The human side of human-chatbot interaction: A systematic literature review of ten years of research on text-based chatbots. International Journal of Human Computer Studies, 151(March), 102630. https://doi.org/10.1016/j.ijhcs.2021.102630

Rojas Vistorte, A. O., Deroncele-Acosta, A., Martín Ayala, J. L., Barrasa, A., López-Granero, C., & Martí-González, M. (2024). Integrating Artificial Intelligence to Assess Emotions in Learning Environments: A Systematic Literature Review. Frontiers in Psychology, 15, 1387089. https://doi.org/https://doi.org/10.3389/fpsyg.2024.1387089

Shah, N. (2024). The unbearable oldness of generative artificial intelligence: Or the re-making of digital narratives in times of ChatGPT. European Journal of Cultural Studies. https://doi.org/10.1177/13675494231223572

Stahl, B. C., & Eke, D. (2024). The ethics of ChatGPT–Exploring the ethical issues of an emerging technology. International Journal of Information Management, 74, 102700.

Sunendar, D., Ismadi, H. D., Amalia, D., Darnis, A. D., & Abdul, G. R. (2020). KBBI Daring. Badan Pengembangan Dan Pembinaan Bahasa.

Tamkin, A., Brundage, M., Clark, J., & Ganguli, D. (2021). Understanding the capabilities, limitations, and societal impact of large language models. ArXiv Preprint ArXiv:2102.02503.

Wei, J., Kim, S., Jung, H., & Kim, Y.-H. (2024). Leveraging large language models to power chatbots for collecting user self-reported data. Proceedings of the ACM on Human-Computer Interaction, 8(CSCW1), 1–35.

Wójcik, S., Rulkiewicz, A., Pruszczyk, P., Lisik, W., Poboży, M., & Domienik-Karłowicz, J. (2023). Beyond ChatGPT: What does GPT-4 add to healthcare? The dawn of a new era. Cardiology Journal, 30(6), 1018–1025. https://doi.org/10.5603/cj.97515

Zhou, K., Yang, J., Loy, C. C., & Liu, Z. (2022). Learning to Prompt for Vision-Language Models. International Journal of Computer Vision, 130(9), 2337–2348. https://doi.org/10.1007/s11263-022-01653-1

Zhu, B., Niu, Y., Han, Y., Wu, Y., & Zhang, H. (2024). Prompt-aligned Gradient for Prompt Tuning. 15613–15623. https://doi.org/10.1109/iccv51070.2023.01435

Zou, B., Guan, X., Shao, Y., & Chen, P. (2023). Supporting speaking practice by social network-based interaction in artificial intelligence (AI)-assisted language learning. Sustainability, 15(4), 2872.

Downloads

Published

2024-07-08

How to Cite

Wildan, M. (2024). Positive and Negative Sentiment of Artificial Intelligence in Language: Study Prompt on Chat GPT. Lexeme : Journal of Linguistics and Applied Linguistics, 6(2), 87–100. https://doi.org/10.32493/ljlal.v6i2.40276

Issue

Section

Articles