Visual Sentiment Analysis from Disaster Images in Social Media

Sensors

Nicola Conci¹  Ala Al-Fuqaha²  Kashif Ahmad²  Steven Hicks³  Pål Halvorsen³  Michael Riegler³  Syed Zohaib Hassan³ 

[1] Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy;Information and Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University (HBKU), Doha 34110, Qatar;SimulaMet, 0167 Oslo, Norway;

关键词: sentiment analysis;    emotions;    deep learning;    multimedia retrieval;    natural disasters;   

DOI  :  10.3390/s22103628

来源: DOAJ

【 摘 要 】

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 the literature, sentiment analysis from images and videos is relatively new. This article focuses on visual sentiment analysis in a societally 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 people’s 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 at a separate task. The presented analysis and the associated dataset, which is made public, 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.

【 授权许可】

Unknown