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...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.展开更多
文摘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.
