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المؤلفPradeep Kumar, Roy
المؤلفKumar, Abhinav
المؤلفSingh, Jyoti Prakash
المؤلفDwivedi, Yogesh Kumar
المؤلفRana, Nripendra Pratap
المؤلفRaman, Ramakrishnan
تاريخ الإتاحة2023-06-08T08:16:22Z
تاريخ النشر2021-09-22
اسم المنشورSustainable Cities and Society
المعرّفhttp://dx.doi.org/10.1016/j.scs.2021.103363
الاقتباسRoy, P. K., Kumar, A., Singh, J. P., Dwivedi, Y. K., Rana, N. P., & Raman, R. (2021). Disaster related social media content processing for sustainable cities. Sustainable Cities and Society, 75, 103363.
الرقم المعياري الدولي للكتاب2210-6707
معرّف المصادر الموحدhttps://www.sciencedirect.com/science/article/pii/S2210670721006387
معرّف المصادر الموحدhttp://hdl.handle.net/10576/44134
الملخصThe current study offers a hybrid convolutional neural networks (CNN) model that filters relevant posts and categorises them into several humanitarian classifications using both character and word embedding of textual content. The distinct embeddings for words and characters are used as input to the CNN model’s various channels. A hurricane, flood, and wildfire dataset are used to validate the proposed model. The model performed similarly across all datasets, with the F1-score ranging from 0.66 to 0.71. Because it uses existing social media posts and may be used as a layer with any social media, the model provides a sustainable solution for disaster analysis. With domain-specific training, the suggested approach can be used to locate useful information in other domains such as traffic accidents and civil unrest also.
اللغةen
الناشرElsevier
الموضوعDisaster
Twitter
Deep learning
CNN
Word embedding
Character embedding
العنوانDisaster related social media content processing for sustainable cities
النوعArticle
رقم المجلد75
ESSN2210-6715
dc.accessType Abstract Only


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