# Visual Sentiment Analysis from Disaster Images in Social Media

arXiv: https://arxiv.org/abs/2009.03051 · HTML: https://mlsanity.com/paper/2009.03051/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2020.

## Built on by (most cited first, 14 of 14)

- [How Good is Google Bard's Visual Understanding? An Empirical Study on Open Challenges](https://mlsanity.com/paper/2307.15016/) (2023)
- [Robust Training of Social Media Image Classification Models for Rapid Disaster Response](https://mlsanity.com/paper/2104.04184/) (2021)
- [Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions](https://mlsanity.com/paper/2110.13980/) (2021)
- [NADBenchmarks -- a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasters](https://mlsanity.com/paper/2212.10735/) (2022)
- [A Named Entity Recognition and Topic Modeling-based Solution for Locating and Better Assessment of Natural Disasters in Social Media](https://mlsanity.com/paper/2405.00903/) (2024)
- [The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions](https://mlsanity.com/paper/2104.02425/) (2021)
- [Floods Relevancy and Identification of Location from Twitter Posts using NLP Techniques](https://mlsanity.com/paper/2301.00321/) (2023)
- [Visual Sentiment Analysis: A Natural DisasterUse-case Task at MediaEval 2021](https://mlsanity.com/paper/2111.11471/) (2021)
- [A Late Fusion Framework with Multiple Optimization Methods for Media Interestingness](https://mlsanity.com/paper/2207.04762/) (2022)
- [VAEER: Visual Attention-Inspired Emotion Elicitation Reasoning](https://mlsanity.com/paper/2505.24342/) (2025)
- [Detecting Damage Building Using Real-time Crowdsourced Images and Transfer Learning](https://mlsanity.com/paper/2110.05762/) (2021)
- [PICS: Pipeline for Image Captioning and Search](https://mlsanity.com/paper/2402.10090/) (2024)
- [Flood of Techniques and Drought of Theories: Emotion Mining in Disasters](https://mlsanity.com/paper/2407.05219/) (2024)
- [Social Media Data Mining of Human Behaviour during Bushfire Evacuation](https://mlsanity.com/paper/2512.01262/) (2025)

## Abstract (arXiv)

The increasing popularity of social networks and users' tendency towards sharing their feelings, expressions, and opinions in text, visual, and audio content, have opened new opportunities and challenges in sentiment analysis. While sentiment analysis of text streams has been widely explored in literature, sentiment analysis from images and videos is relatively new. This article focuses on visual sentiment analysis in a societal important domain, namely disaster analysis in social media. To this aim, we propose a deep visual sentiment analyzer for disaster related images, covering different aspects of visual sentiment analysis starting from data collection, annotation, model selection, implementation, and evaluations. For data annotation, and analyzing peoples' sentiments towards natural disasters and associated images in social media, a crowd-sourcing study has been conducted with a large number of participants worldwide. The crowd-sourcing study resulted in a large-scale benchmark dataset with four different sets of annotations, each aiming a separate task. The presented analysis and the associated dataset will provide a baseline/benchmark for future research in the domain. We believe the proposed system can contribute toward more livable communities by helping different stakeholders, such as news broadcasters, humanitarian organizations, as well as the general public.