Human Sentiment and Activity Recognition in Disaster Situations Using Social Media Images Based on Deep Learning
Autor: | Amin Muhammad Sadiq, Young Bok Choi, Huynsik Ahn |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
Computer science
social media 02 engineering and technology deep fusion Crowdsourcing lcsh:Chemical technology 01 natural sciences Biochemistry Article Analytical Chemistry Social Networking Activity recognition Disasters 0202 electrical engineering electronic engineering information engineering Humans Social media Human Activities lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation human activity analysis business.industry Deep learning 010401 analytical chemistry Sentiment analysis deep learning Data science Atomic and Molecular Physics and Optics 0104 chemical sciences Video tracking sentiment analysis Identity (object-oriented programming) disastrous situations analysis 020201 artificial intelligence & image processing Artificial intelligence business |
Zdroj: | Sensors, Vol 20, Iss 7115, p 7115 (2020) Sensors (Basel, Switzerland) Sensors Volume 20 Issue 24 |
ISSN: | 1424-8220 |
Popis: | A rapidly increasing growth of social networks and the propensity of users to communicate their physical activities, thoughts, expressions, and viewpoints in text, visual, and audio material have opened up new possibilities and opportunities in sentiment and activity analysis. Although sentiment and activity analysis of text streams has been extensively studied in the literature, it is relatively recent yet challenging to evaluate sentiment and physical activities together from visuals such as photographs and videos. This paper emphasizes human sentiment in a socially crucial field, namely social media disaster/catastrophe analysis, with associated physical activity analysis. We suggest multi-tagging sentiment and associated activity analyzer fused with a a deep human count tracker, a pragmatic technique for multiple object tracking, and count in occluded circumstances with a reduced number of identity switches in disaster-related videos and images. A crowd-sourcing study has been conducted to analyze and annotate human activity and sentiments towards natural disasters and related images in social networks. The crowdsourcing study outcome into a large-scale benchmark dataset with three annotations sets each resolves distinct tasks. The presented analysis and dataset will anchor a baseline for future research in the domain. We believe that the proposed system will contribute to more viable communities by benefiting different stakeholders, such as news broadcasters, emergency relief organizations, and the public in general. |
Databáze: | OpenAIRE |
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