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Visual Sentiment Analysis from Disaster Images in Social Media.

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Summary
This summary is machine-generated.

This study introduces a deep visual sentiment analyzer for disaster images shared on social media. It presents a new benchmark dataset to advance visual sentiment analysis in disaster contexts.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Social Media Analysis

Background:

  • Social media fuels sentiment analysis with diverse content, yet visual sentiment analysis, especially for disaster events, remains underexplored.
  • Analyzing public sentiment in disaster imagery is crucial for humanitarian aid and disaster response.
  • Existing research primarily focuses on text-based sentiment analysis, leaving a gap in visual data interpretation.

Purpose of the Study:

  • To develop and evaluate a deep visual sentiment analyzer specifically for disaster-related images.
  • To create a large-scale, publicly available benchmark dataset for visual sentiment analysis in disaster contexts.
  • To provide a foundational resource for future research in social media disaster analysis.

Main Methods:

  • A deep learning model was designed for visual sentiment analysis of disaster imagery.
  • A global crowd-sourcing study was conducted for data annotation and sentiment labeling.
  • A benchmark dataset with multiple annotation sets was created for various analytical tasks.

Main Results:

  • A novel deep visual sentiment analyzer for disaster images was successfully developed.
  • A comprehensive benchmark dataset was generated through a large-scale crowd-sourcing effort.
  • The study established a baseline for visual sentiment analysis in the domain of social media disaster response.

Conclusions:

  • The proposed deep visual sentiment analyzer and benchmark dataset advance the field of visual sentiment analysis in disaster contexts.
  • This research offers valuable tools for stakeholders like humanitarian organizations and news broadcasters.
  • The findings contribute to building more resilient communities by improving disaster information analysis.