  With the development of the Internet, social media has become an important
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 attitudes towards disaster response and analyzing targeted relief
supplies during disaster response. The contributions of this paper are
fourfold. First, we propose several machine learning models for classifying
public sentiment concerning disaster-related social media data. Second, we
create a natural disaster dataset with sentiment labels, which contains nearly
50,00 Twitter data about different natural disasters in the United States
(e.g., a tornado in 2011, a hurricane named Sandy in 2012, a series of floods
in 2013, a hurricane named Matthew in 2016, a blizzard in 2016, a hurricane
named Harvey in 2017, a hurricane named Michael in 2018, a series of wildfires
in 2018, and a hurricane named Dorian in 2019). We are making our dataset
available to the research community:
https://github.com/Dong-UTIL/Natural-Hazards-Twitter-Dataset. It is our hope
that our contribution will enable the study of sentiment analysis in disaster
response. Third, we focus on extracting public attitudes and analyzing the
essential needs (e.g., food, housing, transportation, and medical supplies) for
the public during disaster response, instead of merely targeting on studying
positive or negative attitudes of the public to natural disasters. Fourth, we
conduct this research from two different dimensions for a comprehensive
understanding of public opinion on disaster response, since disparate hazards
caused by different types of natural disasters.
