Deep Models for Visual Sentiment Analysis of Disaster-related Multimedia Content

This paper presents a solutions for the MediaEval 2021 task namely”Visual Sentiment Analysis: A Natural Disaster Use-case”. The task aims to extract and classify sentiments perceived by viewers and the emotional message conveyed by natural disaster-related images shared on social media. The task is composed of three sub-tasks including, one single label multi-class image classification subtask, and, two multi-label multi-class image classification subtasks. Both the multi-label classification tasks cover different sets of labels. In our proposed solutions, we mainly rely on two different state-of-the-art models namely, Inception-v3 and VggNet-19, pre-trained on ImageNet. Both the pre-trained models are fine-tuned for each of the three subtasks using different strategies. Overall encouraging results are obtained on all of the three subtasks. On the single-label classification subtask (i.e. subtask 1), we obtained the weighted average F1-scores of 0.540 and 0.526 for the Inception-v3 and VggNet-19 based solutions, respectively. On the multi-label classification tasks i.e., subtask 2 and subtask 3, the weighted F1-scores of our Inceptionv3 based solutions are 0.572 and 0.516, respectively. Similarly, the weighted F1-scores of our VggNet-19 based solution on the subtask 2 and subtask 3 are 0.584 and 0.495, respectively.