Adaptive Visual Sentiment Prediction Model Based on Event Concepts and Object Detection Techniques in Social Media

Now-a-days, the increasing number of smartphones has caused the immediate sharing of photographs capturing current events on social media. The sentimental content of pictures from social events starts to be obtained from visual material, so visual sentiment analysis is a vital research topic. The research aims to reach valuable criteria to modify the visual sentiment prediction model based on event concepts and object detection techniques. In addition to adapting the approach for designing the method for predicting visual sentiments in a social network according to concept scores and measuring the performance of the model for predicting visual sentiments as accurately as possible, approach obtains a visual summary of social event images based on the visual elements that appear in the pictures which exceed sentiment-specific features. By this method, attributes (color, texture) are assigned to sentiments with discovering affective objects that are used to obtain emotions related to a picture of a social event by mapping the top predicted qualities to feelings and extracting the prevailing emotion connected with a photograph of a social event. This method is valid for a wide range of social events. This strategy also demonstrates the social event's effectiveness for a difficult social event image collection by using techniques for classifying complicated event images into sentiments, whether positive or negative.

@article{Fouad2023,
  title     = {Adaptive Visual Sentiment Prediction Model Based on Event Concepts and Object Detection Techniques in Social Media},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {7},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Yasser Fouad and Ahmed M. Osman and Samah A. Z. Hassan and Hazem M. El-Bakry and Ahmed M. Elshewey},
  doi       = {10.14569/IJACSA.2023.0140728},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140728}
}