Published November 30, 2023 | Version CC-BY-NC-ND 4.0
Journal Open

Artificial Intelligence-based Hyper Activity Analysis

Creators

  • 1. Department of Computer Science, SGT, University, Haryana, Gurugram (Haryana), India.

Contributors

Contact person:

Researcher:

  • 1. Department of Computer Science, SGT, University, Haryana, Gurugram (Haryana), India.

Description

Abstract: Sentiment analysis has emerged as a valuable tool for analyzing human behavior and measuring frustration levels. This abstract provides an overview of the sentiment analysis of human behavior in response tomeasuring frustration levels. By examining the emotional tone expressed in textual data, sentiment analysis techniques offer insights into individuals' frustration levels, contributing to a better understanding of their psychological wellbeing. This study focuses on the application of sentiment analysis in measuring frustration levels and understanding human behavior. It explores the limitations and challenges associated with accurately assessing frustration based on Textual data, Psychological Questionnaires, Image Processing, Tone or speech, and Augmented Reality. The study acknowledges the importance ofcontext and the need to account for linguistic nuances, sarcasm, and individual differences in language use. It also emphasizes the significance of considering additional modalities, such as facial expressions and virtual reality, to enhance the accuracy and reliability of measuring frustration levels.

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Additional details

Identifiers

DOI
10.35940/ijaent.F4237.11101123
ISSN
2347-6389

Dates

Accepted
2023-11-15
Manuscript received on 21 June 2023 | Revised Manuscript received on 09 November 2023 | Manuscript Accepted on 15 November 2023 | Manuscript published on 30 November 2023

References

  • Lamberti, Marc. "Project Report Twitter Emotion Analysis." Supervised by David Rossiter, The Hong Kong University of Science and Technology, www.cse.ust.hk/~rossiter/independent_studies_projects/twitter_emotio n_analysis/twitter_emotion_anal ysis.pdf.
  • "Sentiment Analysis." Sentiment Analysis, Wikipedia, https://en.wikipedia.org/wiki/Sentiment_analysis.
  • Liu, Bing. "Sentiment Analysis and Subjectivity." University of Illinois at Chicago, University of Illinois at Chicago, 2010, www.cs.uic.edu/~liub/FBS/NLP-handbook-sentiment-analysis.pdf.
  • Cambria, E., & Hussain, A. (2012). SenticNet 2: A semantic and affective resource for sentiment analysis and opinion mining. IEEE Transactions on Affective Computing, 3(1), 1-15. doi:10.1109/TAFFC. 2011.15 Conference paper: Author(s). (Year, Month). Title of the paper. Paper presented at Conference Name, Location. DOI (if available).
  • Pang, B., & Lee, L. (2008, July). Opinion mining and sentiment analysis. In Proceedings of the Association for Computational Linguistics (ACL) (pp. 79-86). Columbus, Ohio, USA. doi:10.3115/1570079.1570106 Book: Author(s). (Year). Title of the book. Publisher.
  • Liu, B. (2012). Sentiment analysis and opinion mining. Morgan & Claypool Publishers.Thesis or dissertation: Author. (Year). Title of the thesis/dissertation. (Unpublished doctoral dissertation/Master's thesis). Institution.
  • Smith, J. D. (2015). Sentiment analysis of social media data (Unpublished doctoral dissertation). University of California, Berkeley Website: Author(s)/Organization. (Year, Month). Title of the webpage/document. Website Name. Retrieved from URL
  • Pew Research Center. (2021, October). Social media use in 2021. Pew Research Center. Retrieved from https://www.pewresearch.org/internet/2021/10/12/social-media-usein- 2021/
  • Geetha, V., Gomathy, C. K., Manojkumar, P., & Manohar, N. S. L. S. V. (2020). The Sentimental Analysis for E-Commerce Application. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 5, pp. 1232–1236). https://doi.org/10.35940/ijeat.d8717.069520
  • R G, K., & R, D. S. (2019). Sentiment Analysis using Bi-directional Recurrent Neural Network for Telugu Movies. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 2, pp. 241–245). https://doi.org/10.35940/ijitee.b6137.129219
  • Zainudin*, M. N. S., Kee, Y. J., Idris, M. I., Kamaruddin, M. R., & Ramlee, R. H. (2019). Recognizing the Activity Daily Living (ADL) for Subject Independent. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 3, pp. 5422–5427). https://doi.org/10.35940/ijrte.b2381.098319
  • Bilog, R. J. (2020). Application of Naïve Bayes Algorithm in Sentiment Analysis of Filipino, English and Taglish Facebook Comments. In International Journal of Management and Humanities (Vol. 4, Issue 5, pp. 73–77). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP. https://doi.org/10.35940/ijmh.e0524.014520
  • Patil, M. M., Nikumbh, S. N., & Parigond, A. P. (2021). Fake Product Monitoring and Removal for Genuine Product Feedback. In Regular Issue (Vol. 7, Issue 1, pp. 1–3). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP. https://doi.org/10.35940/ijese.a2494.037121