Before revision:  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.
After revision:  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 public fear. Public attitudes to natural disasters are studied via a quantitative analysis using eight machine learning models for sentiment prediction, which are the most popular models used in classification problems.