论文标题
自然危害Twitter数据集
Natural Hazards Twitter Dataset
论文作者
论文摘要
随着互联网的发展，社交媒体已成为发布与灾难有关的信息的重要渠道。分析这些文本中隐藏的态度（称为情感分析）对于提高灾难反应效率的政府或救援机构至关重要，但尚未得到足够的关注。本文的目的是通过专注于调查对灾害反应的态度并分析灾难响应期间有针对性的救济用品的态度来填补这一空白。本文的贡献是四倍。首先，我们提出了几种机器学习模型，用于对与灾难相关的社交媒体数据进行分类。 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年，飓风在2019年被称为多利安（Dorian））。我们正在向研究社区提供数据集：https：//github.com/dong--util/natural-hazards-twitter-dataset。我们希望我们的贡献能够在灾难响应中对情感分析进行研究。第三，我们专注于在灾难响应期间为公众提出公众态度，并分析公众的基本需求（例如食品，住房，运输和医疗用品），而不仅仅是针对研究公众对自然灾害的积极或负面态度。第四，我们从两个不同的维度进行了这项研究，以便全面理解公众对灾难反应的理解，因为不同类型的自然灾害造成的不同危害。
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.
