The climate change Twitter dataset

作者：

Highlights：

• Create the most extensive dataset for climate change and human opinions via Twitter.

• Make it publicly available.

• Link 7 dimensions of information to each of the 15 million geolocated tweets.

• Include Gender, Stance, Sentiment, Aggressiveness, Temperature, Topics, Disasters.

• Use of BERT, RNN, LSTM, CNN, SVM, Naive Bayes, VADER, Textblob, Flair, and LDA.

摘要

•Create the most extensive dataset for climate change and human opinions via Twitter.•Make it publicly available.•Link 7 dimensions of information to each of the 15 million geolocated tweets.•Include Gender, Stance, Sentiment, Aggressiveness, Temperature, Topics, Disasters.•Use of BERT, RNN, LSTM, CNN, SVM, Naive Bayes, VADER, Textblob, Flair, and LDA.

论文关键词：Climate change,Machine learning,Sentiment analysis,Topic modeling,Twitter

论文评审过程：Received 20 October 2021, Revised 15 January 2022, Accepted 6 May 2022, Available online 14 May 2022, Version of Record 23 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117541