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

arXiv: https://arxiv.org/abs/2112.12060 · HTML: https://mlsanity.com/paper/2112.12060/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Natural Disasters Detection in Social Media and Satellite imagery: a survey](https://mlsanity.com/paper/1901.04277/) (2019)
- [Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response](https://mlsanity.com/paper/2011.08916/) (2020)
- [Sentiment Analysis from Images of Natural Disasters](https://mlsanity.com/paper/1910.04416/) (2019)
- [Active Learning for Event Detection in Support of Disaster Analysis Applications](https://mlsanity.com/paper/1909.12601/) (2019)
- [Visual Sentiment Analysis: A Natural DisasterUse-case Task at MediaEval 2021](https://mlsanity.com/paper/2111.11471/) (2021)

## Abstract (arXiv)

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 task, and, two multi-label multi-class image classification tasks, with different sets of labels. In our proposed solutions, we rely mainly on two different state-of-the-art models namely, Inception-v3 and VggNet-19, pre-trained on ImageNet, which are fine-tuned for each of the three task using different strategies. Overall encouraging results are obtained on all the three tasks. On the single-label classification task (i.e. Task 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 i.e., Task 2 and Task 3, the weighted F1-score of our Inception-v3 based solutions was 0.572 and 0.516, respectively. Similarly, the weighted F1-score of our VggNet-19 based solution on Task 2 and Task 3 was 0.584 and 0.495, respectively.