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Research Article

Multimodal Sentiment Analysis in Natural Disaster Data on Social Media

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  • @ARTICLE{10.4108/eetsc.5860,
        author={Sefa Dursun and S\'{y}leyman Eken},
        title={Multimodal Sentiment Analysis in Natural Disaster Data on Social Media},
        journal={EAI Endorsed Transactions on Smart Cities},
        volume={7},
        number={4},
        publisher={EAI},
        journal_a={SC},
        year={2024},
        month={11},
        keywords={Multimodal Learning, Sentiment Analysis, Natural Disaster, Natural Language Processing, Image Processing},
        doi={10.4108/eetsc.5860}
    }
    
  • Sefa Dursun
    Süleyman Eken
    Year: 2024
    Multimodal Sentiment Analysis in Natural Disaster Data on Social Media
    SC
    EAI
    DOI: 10.4108/eetsc.5860
Sefa Dursun1, Süleyman Eken1,*
  • 1: Kocaeli Üniversitesi
*Contact email: suleyman.eken@kocaeli.edu.tr

Abstract

INTRODUCTION: With the development of the Internet, users tend to express their opinions and emotions through text, visual and/or audio content. This has increased the interest in multimodal analysis methods. OBJECTIVES: This study addresses multimodal sentiment analysis on tweets related to natural disasters by combining textual and visual embeddings. METHODS: The use of textual representations together with the emotional expressions of the visual content provides a more comprehensive analysis. To investigate the impact of high-level visual and texual features, a three-layer neural network is used in the study, where the first two layers collect features from different modalities and the third layer is used to analyze sentiments. RESULTS: According to experimental tests on our dataset, the highest performance values (77% Accuracy, 71% F1-score) are achieved by using the CLIP model in the image and the RoBERTa model in the text. CONCLUSION: Such analyzes can be used in different application areas such as agencies, advertising, social/digital media content producers, humanitarian aid organizations and can provide important information in terms of social awareness.

Keywords
Multimodal Learning, Sentiment Analysis, Natural Disaster, Natural Language Processing, Image Processing
Received
2024-11-14
Accepted
2024-11-14
Published
2024-11-14
Publisher
EAI
http://dx.doi.org/10.4108/eetsc.5860

Copyright © 2024 S. Dursun and S. Eken, licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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