most citedDeriving Emotions and Sentiments from Visual Content: A Disaster Analysis Use Case

5 citations · 17 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV20203 cited

Flood Detection via Twitter Streams using Textual and Visual Features

Firoj Alam, Zohaib Hassan, Kashif Ahmad +4

The paper presents our proposed solutions for the MediaEval 2020 Flood-Related Multimedia Task, which aims to analyze and detect flooding events in multimedia content shared over T…

cs.CV20203 cited

Visual Sentiment Analysis from Disaster Images in Social Media

Syed Zohaib Hassan, Kashif Ahmad, Steven Hicks +4

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 oppor…

cs.CV20205 cited

Deriving Emotions and Sentiments from Visual Content: A Disaster Analysis Use Case

Kashif Ahmad, Syed Zohaib, Nicola Conci +1

Sentiment analysis aims to extract and express a person's perception, opinions and emotions towards an entity, object, product and a service, enabling businesses to obtain feedback…

cs.CV2019

Sentiment Analysis from Images of Natural Disasters

Syed Zohaib, Kashif Ahmad, Nicola Conci +1

Social media have been widely exploited to detect and gather relevant information about opinions and events. However, the relevance of the information is very subjective and rather…

cs.CV20194 cited

Multi-Modal Machine Learning for Flood Detection in News, Social Media and Satellite Sequences

Kashif Ahmad, Konstantin Pogorelov, Mohib Ullah +4

In this paper we present our methods for the MediaEval 2019 Mul-timedia Satellite Task, which is aiming to extract complementaryinformation associated with adverse events from Soci…

cs.CV20192 cited

Active Learning for Event Detection in Support of Disaster Analysis Applications

Naina Said, Kashif Ahmad, Nicola Conci +1

Disaster analysis in social media content is one of the interesting research domains having abundance of data. However, there is a lack of labeled data that can be used to train ma…