Improving Visual Sentiment Analysis in Social Networks using Transfer Learning Models

Analyzing individuals' emotions from the content of social media through text, speech, and images is necessary for various types of applications and purposes. Most recent research studies in the field of sentiment analysis have focused on textual data. However, social media users share more images and videos compared to text. In other words, images are the most effective way to convey emotions to others. Therefore, focusing on the development of a sentiment analysis model based on images in social media is important. In this article, we will use the DenseNet-121 transfer learning model to analyze emotions based on images. To implement this approach, we will utilize the images available in the Image Sentiment dataset. This dataset includes internet links to images along with their emotional polarities. Based on the obtained results, the accuracy of the proposed model in this article is 89%, which, compared to previous work in the field of visual sentiment analysis, shows a 5% to 10% improvement.