TY  - EJOU
AU  - Afrifa, Stephen 
AU  - Varadarajan, Vijayakumar 
AU  - Appiahene, Peter 
AU  - Zhang, Tao 
AU  - Afrifa, Richmond 

TI  - Evaluating Public Sentiments during Uttarakhand Flood: An Artificial Intelligence Techniques
T2  - Computer Systems Science and Engineering

PY  - 2024
VL  - 48
IS  - 6
SN  - 

AB  - Users of social networks can readily express their thoughts on websites like Twitter (now X), Facebook, and Instagram. The volume of textual data flowing from users has greatly increased with the advent of social media in comparison to traditional media. For instance, using natural language processing (NLP) methods, social media can be leveraged to obtain crucial information on the present situation during disasters. In this work, tweets on the Uttarakhand flash flood are analyzed using a hybrid NLP model. This investigation employed sentiment analysis (SA) to determine the people’s expressed negative attitudes regarding the disaster. We apply a machine learning algorithm and evaluate the performance using the standard metrics, namely root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Our random forest (RF) classifier outperforms comparable works with an accuracy of 98.10%. In order to gain a competitive edge, the study shows how Twitter (now X) data and machine learning (ML) techniques can analyze public discourse and sentiments regarding disasters. It does this by comparing positive and negative comments in order to develop strategies to deal with public sentiments on disasters.
KW  - Artificial intelligence; natural language processing; machine learning; social media; multimedia; disaster

DO  - 10.32604/csse.2024.055084