A DECOC-Based Classifier for Analyzing Emotional Expressions in Emoji Usage on Social Media

In today's digital era, social media has profoundly transformed communication, enabling new forms of emotional expression through various tools, particularly emojis. Initially created to represent simple emotions, emojis have evolved into a rich and nuanced visual language capable of conveying complex emotional states. While their role in communication is well-documented, there remains a gap in effectively analyzing and interpreting the emotional subtleties conveyed through emojis. This paper presents an innovative approach to sentiment analysis that goes beyond conventional methods by integrating a machine learning model, specifically the DECOC (Error Correcting Output Codes) classifier, tailored for the combined analysis of text and emoji sequences. The proposed model addresses the limitations of existing methods, which often overlook the sequential and contextual nature of emojis in emotional expression. By applying this model to real-world data, including a survey of social media users in Saudi Arabia, we demonstrate its high efficacy, achieving an average accuracy of 94.76%. This result not only outperforms prior models but also validates the significance of treating emojis as fundamental components of digital sentiment analysis. Our findings underscore the critical need for advanced models to decode the emotional layers of emoji usage, offering deeper insights into their role in contemporary digital communication.

@article{Alshaya2024,
  title     = {A DECOC-Based Classifier for Analyzing Emotional Expressions in Emoji Usage on Social Media},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Shaya A. Alshaya},
  doi       = {10.14569/IJACSA.2024.0151083},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151083}
}