The climate change Twitter dataset

This work creates and makes publicly available the most comprehensive dataset to date regarding climate change and human opinions via Twitter. It has the heftiest temporal coverage, spanning over 13 years, includes over 15 million tweets spatially distributed across the world, and provides the geolocation of most tweets. Seven dimensions of information are tied to each tweet, namely geolocation, user gender, climate change stance and sentiment, aggressiveness, deviations from historic temperature, and topic modeling, while accompanied by environmental disaster events information. These dimensions were produced by testing and evaluating a plethora of state-of-the-art machine learning algorithms and methods, both supervised and unsupervised, including BERT, RNN, LSTM, CNN, SVM, Naive Bayes, VADER, Textblob, Flair, and LDA. • 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.