Vol. 1 No. 7 (2024): Issue Month: December, 2024
Journal Article

A Machine Learning-Based IRIS Tracking System for Predicting Emotions from Social Media Interactions

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  • Prof. Ritumbara Chauhan,
  • Prof. Harleen Kaur
Prof. Ritumbara Chauhan
IPS Academy, Indore, Madhya Pradesh, India
Prof. Harleen Kaur
IPS Academy, Indore, Madhya Pradesh, India
DOI: https://doi.org/10.61359/11.2206-2434
Categories

Published 2024-12-30

Keywords

  • IRIS Tracking,
  • Emotion Prediction,
  • Machine Learning,
  • Social Media,
  • Eye-Tracking

How to Cite

Prof. Ritumbara Chauhan, & Prof. Harleen Kaur. (2024). A Machine Learning-Based IRIS Tracking System for Predicting Emotions from Social Media Interactions. International Journal of Advanced Research and Interdisciplinary Scientific Endeavours, 1(7), 357–361. https://doi.org/10.61359/11.2206-2434

Copyright (c) 2025 International Journal of Advanced Research and Interdisciplinary Scientific Endeavours

Creative Commons License

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

Abstract

This paper presents an efficient iris tracking system for emotion prediction in social media using machine learning techniques. We propose a novel approach to track iris movements and correlate them with emotional states expressed in online content. The system employs machine learning models, including support vector machines (SVMs) and deep neural networks, to predict emotions based on eye movements and pupil dilation. The system's performance was evaluated on a dataset of eye-tracking data from social media interactions, yielding an accuracy of 85%. The findings suggest that this approach could enhance emotion recognition capabilities in social media platforms, offering a new dimension in personalized user experience.

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