TY - JOUR
T1 - Visual Sentiment Analysis
T2 - MediaEval 2021 Workshop, MediaEval 2021
AU - Hassan, Syed Zohaib
AU - Ahmad, Kashif
AU - Riegler, Michael A.
AU - Hicks, Steven
AU - Conci, Nicola
AU - Halvorsen, Pål
AU - Al-Fuqaha, Ala
N1 - Publisher Copyright:
Copyright 2021 for this paper by its authors.
PY - 2021
Y1 - 2021
N2 - The Visual Sentiment Analysis task is being offered for the first time at MediaEval. The main purpose of the task is to predict the emotional response to images of natural disasters shared on social media. Disaster-related images are generally complex and often evoke an emotional response, making them an ideal use case of visual sentiment analysis. We believe being able to perform meaningful analysis of natural disaster-related data could be of great societal importance, and a joint effort in this regard can open several interesting directions for future research. The task is composed of three sub-tasks, each aiming to explore a different aspect of the challenge. In this paper, we provide a detailed overview of the task, the general motivation of the task, and an overview of the dataset and the metrics to be used for the evaluation of the proposed solutions.
AB - The Visual Sentiment Analysis task is being offered for the first time at MediaEval. The main purpose of the task is to predict the emotional response to images of natural disasters shared on social media. Disaster-related images are generally complex and often evoke an emotional response, making them an ideal use case of visual sentiment analysis. We believe being able to perform meaningful analysis of natural disaster-related data could be of great societal importance, and a joint effort in this regard can open several interesting directions for future research. The task is composed of three sub-tasks, each aiming to explore a different aspect of the challenge. In this paper, we provide a detailed overview of the task, the general motivation of the task, and an overview of the dataset and the metrics to be used for the evaluation of the proposed solutions.
UR - http://www.scopus.com/inward/record.url?scp=85137015080&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85137015080
SN - 1613-0073
VL - 3181
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
Y2 - 13 December 2021 through 15 December 2021
ER -