International Journal For Multidisciplinary Research

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Machine Learning & Deep Learning: Classifying Disaster Tweets Methods

Author(s) Rohan Nadekar, Saif Kumbay, Jayesh Walke, Najim tadvi, Arnav Mone, Dr Manisha Mali
Country India
Abstract With the increasing reliance on social media sites, and more importantly on Twitter, during disaster events, there is a strong need for an immediate capability to effectively analyze large quantities of near-real-time data into meaningful information in support of more efficient emergency responses. This paper reviews new trends in disaster tweet classification using machine learning and deep learning. We have picked five major studies with approaches ranging from more basic approaches to more complex approaches with Logistic Regression and Naive Bayes to using CNN and BERT deep architectures. Findings of this study include apparent flips in the use of BERT embeddings for improving accuracy and relevance of disaster-related tweet classification, which essentially provides emergency responders with timely and critical information when crises arise.. Review work calls for the input of advanced deep learning techniques to answer the complexities involved with the real-time data streaming that characterizes a disaster scenario.
Keywords Disaster response, tweet classification, machine learning, deep learning, BERT embeddings, social media analytics
Field Engineering
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-11-25
Cite This Machine Learning & Deep Learning: Classifying Disaster Tweets Methods - Rohan Nadekar, Saif Kumbay, Jayesh Walke, Najim tadvi, Arnav Mone, Dr Manisha Mali - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.29975
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.29975
Short DOI https://doi.org/g8r8k6
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