| Title |
The climate change Twitter dataset |
| ID_Doc |
64473 |
| Authors |
Effrosynidis, D; Karasakalidis, A; Sylaios, G; Arampatzis, A |
| Title |
The climate change Twitter dataset |
| Year |
2022 |
| Published |
|
| DOI |
10.1016/j.eswa.2022.117541 |
| Abstract |
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. |
| Author Keywords |
Climate change; Machine learning; Sentiment analysis; Topic modeling; Twitter |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000819313900007 |
| WoS Category |
Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science |
| Research Area |
Computer Science; Engineering; Operations Research & Management Science |
| PDF |
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