Mining Public Opinion on Twitter about Natural Disaster Response Using Machine Learning Techniques
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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.
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