Visual Sentiment Analysis from Disaster Images in Social Media
<div><p>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 wide...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2022
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513517941424128 |
|---|---|
| author | Syed Zohaib Hassan (18387129) |
| author2 | Kashif Ahmad (12592762) Steven Hicks (3443687) Pål Halvorsen (6595853) Ala Al-Fuqaha (4434340) Nicola Conci (16362714) Michael Riegler (5583212) |
| author2_role | author author author author author author |
| author_facet | Syed Zohaib Hassan (18387129) Kashif Ahmad (12592762) Steven Hicks (3443687) Pål Halvorsen (6595853) Ala Al-Fuqaha (4434340) Nicola Conci (16362714) Michael Riegler (5583212) |
| author_role | author |
| dc.creator.none.fl_str_mv | Syed Zohaib Hassan (18387129) Kashif Ahmad (12592762) Steven Hicks (3443687) Pål Halvorsen (6595853) Ala Al-Fuqaha (4434340) Nicola Conci (16362714) Michael Riegler (5583212) |
| dc.date.none.fl_str_mv | 2022-05-10T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/s22103628 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Visual_Sentiment_Analysis_from_Disaster_Images_in_Social_Media/25688787 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Computer vision and multimedia computation sentiment analysis emotions deep learning multimedia retrieval natural disasters |
| dc.title.none.fl_str_mv | Visual Sentiment Analysis from Disaster Images in Social Media |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>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.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Sensors<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s22103628" target="_blank">https://dx.doi.org/10.3390/s22103628</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_30883c1c8a334a0586095d477cf1e13c |
| identifier_str_mv | 10.3390/s22103628 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25688787 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Visual Sentiment Analysis from Disaster Images in Social MediaSyed Zohaib Hassan (18387129)Kashif Ahmad (12592762)Steven Hicks (3443687)Pål Halvorsen (6595853)Ala Al-Fuqaha (4434340)Nicola Conci (16362714)Michael Riegler (5583212)Information and computing sciencesComputer vision and multimedia computationsentiment analysisemotionsdeep learningmultimedia retrievalnatural disasters<div><p>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.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Sensors<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/s22103628" target="_blank">https://dx.doi.org/10.3390/s22103628</a></p>2022-05-10T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/s22103628https://figshare.com/articles/journal_contribution/Visual_Sentiment_Analysis_from_Disaster_Images_in_Social_Media/25688787CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/256887872022-05-10T03:00:00Z |
| spellingShingle | Visual Sentiment Analysis from Disaster Images in Social Media Syed Zohaib Hassan (18387129) Information and computing sciences Computer vision and multimedia computation sentiment analysis emotions deep learning multimedia retrieval natural disasters |
| status_str | publishedVersion |
| title | Visual Sentiment Analysis from Disaster Images in Social Media |
| title_full | Visual Sentiment Analysis from Disaster Images in Social Media |
| title_fullStr | Visual Sentiment Analysis from Disaster Images in Social Media |
| title_full_unstemmed | Visual Sentiment Analysis from Disaster Images in Social Media |
| title_short | Visual Sentiment Analysis from Disaster Images in Social Media |
| title_sort | Visual Sentiment Analysis from Disaster Images in Social Media |
| topic | Information and computing sciences Computer vision and multimedia computation sentiment analysis emotions deep learning multimedia retrieval natural disasters |