An Efficient Multi-Modal Classification Approach for Disaster-related Tweets
- Title
- An Efficient Multi-Modal Classification Approach for Disaster-related Tweets
- Creator
- Mondal A.; Kesan A.; Rodrigues A.; George J.
- Description
- Owing to the unanticipated and thereby treacherous nature of disasters, it is essential to gather necessary information and data regarding the same on an urgent basis; this helps to get a detailed overview of the situation and helps humanitarian organizations prioritize their tasks. In this paper, "An Efficient Multi-Modal Classification Approach for Disaster-related Tweets,"the proposed framework based on Deep Learning to classify disaster-related tweets by analyzing text and image contents. The approach is based on Gated Recurrent Unit (GRU) and GloVe Embedding for text classification and VGG-16 network for image classification. Finally, a combined model is proposed using both text and image modules by the Late Fusion Technique. This portrays that the proposed multi-modal system performs significantly well in classifying disaster-related content. 2022 IEEE.
- Source
- IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022
- Date
- 2022-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Convolution Neural Network; Deep Learning; Disaster Tweet; Gated Recurrent Unit; Late Fusion; VGG-16
- Coverage
- Mondal A., Deemed to Be University, Department of Data Science Christ, India; Kesan A., Deemed to Be University, Department of Data Science Christ, India; Rodrigues A., Deemed to Be University, Department of Data Science Christ, India; George J., Deemed to Be University, Department of Data Science Christ, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-166548316-2
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Mondal A.; Kesan A.; Rodrigues A.; George J., “An Efficient Multi-Modal Classification Approach for Disaster-related Tweets,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/20324.