Artificial Intelligence-based Hyper Activity Analysis
Sanjay Kumar Jha1, Rakhi2

1Sanjay Kumar Jha, Department of Computer Science, SGT, University, Haryana, Gurugram (Haryana), India.

2Rakhi, Department of Computer Science, SGT, University, Haryana, Gurugram (Haryana), India.

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 | PP: 5-11 | Volume-10 Issue-11, November 2023 | Retrieval Number: 100.1/ijaent.F42370812623 | DOI: 10.35940/ijaent.F4237.11101123

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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 of context 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.

Keywords: Distress Detection by Facial Expression; Distress detection by Psychological Questionnaire; Distress Detection By Text; Distress Detection by AR; Multidimensional Distress Detection Model
Scope of the Article: Artificial Intelligence