With the development of the Internet, social media has become an essential channel for posting disaster-related information . Analyzing attitudes hidden in these texts, known as sentiment analysis, is crucial for the government or relief agencies to improve disaster response efficiency, but it has not received sufficient attention  . This paper aims to fill this gap by focusing on investigating public attitudes towards disaster response and analyzing targeted relief supplies during disaster relief. The research comprises four steps. First, this paper implements Python in grasping Twitter data, and then, we assess public perceptron quantitatively by these opinioned texts ,  which contain information like the demand for targeted relief supplies, satisfactions of disaster response and fear of the public . A natural disaster dataset with sentiment labels is created, which contains 49,816 Twitter data about natural disasters in the United States. Second, this paper proposes eight machine learning models for sentiment prediction, which are the most popular models used in classification problems. Third , the comparison of these models is conducted via various metrics, and this paper also discusses the optimization method of these models from the perspective of model parameters and input data structures. Finally, a set of real-world instances are studied from the perspective of analyzing changes of public opinion during different natural disasters and understanding the relationship between the same hazard and time series. Results in this paper demonstrate the feasibility and validation of the proposed research approach and provide relief agencies with insights into better disaster response .
Social media has become an essential channel for posting disaster-related information , which provide governments and relief agencies real-time data for better disaster management. However, research in this field has not received sufficient attention and extracting useful information is still challenging . This paper aims to improve disaster relief efficiency via mining and analyzing social media data like public attitudes towards disaster response and public demands for targeted relief supplies during different types of disasters. We focus on different natural disasters based on properties such as types, durations, and damages, which contains a total of 41 , 993 tweets. In this paper, public perception is assessed qualitatively by manually classified tweets, which contain information like the demand for targeted relief supplies, satisfactions of disaster response , and public fear. Public attitudes to natural disasters are studied via a quantitative analysis using eight machine learning models . To better provide decision-makers with the appropriate model , the comparison of machine learning models based on computational time and prediction accuracy is conducted. The change of public opinion during different natural disasters and the evolution of people's behavior of using social media for disaster relief in the face of the identical type of natural disasters as Twitter continues to evolve are studied. The results in this paper demonstrate the feasibility and validation of the proposed research approach and provide relief agencies with insights into better disaster management .
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With the development of the Internet, social
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. Analyzing attitudes hidden in these texts, known as sentiment analysis, is crucial for the government or relief agencies to improve disaster response efficiency, but it
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and extracting useful information is still challenging
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fill this gap by focusing on investigating
improve disaster relief efficiency via mining and analyzing social media data like
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disaster relief. The research comprises four steps. First, this paper implements Python in grasping Twitter data, and then, we assess public perceptron quantitatively by these opinioned texts
different types of disasters. We focus on different natural disasters based on properties such as types, durations, and damages, which contains a total of 41
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993 tweets. In this paper, public perception is assessed qualitatively by manually classified tweets,
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and fear of the public . A natural disaster dataset with sentiment labels is created, which contains 49,816 Twitter data about natural disasters in the United States. Second, this paper proposes
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for sentiment prediction, which are the most popular models used in classification problems. Third
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these models is conducted via various metrics, and this paper also discusses the optimization method of these models from the perspective of model parameters and input data structures. Finally, a set of real-world instances are studied from the perspective of analyzing changes of
machine learning models based on computational time and prediction accuracy is conducted. The change of
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understanding the relationship between the same hazard and time series. Results
the evolution of people's behavior of using social media for disaster relief in the face of the identical type of natural disasters as Twitter continues to evolve are studied. The results
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response
management