Research Article
Application of Traditional and Deep Learning Algorithms in Sentiment Analysis of Global Warming Tweets
@INPROCEEDINGS{10.1007/978-3-031-52524-7_4, author={Dragana Nikolova and Georgina Mircheva and Eftim Zdravevski}, title={Application of Traditional and Deep Learning Algorithms in Sentiment Analysis of Global Warming Tweets}, proceedings={Smart Objects and Technologies for Social Good. 9th EAI International Conference, GOODTECHS 2023, Leiria, Portugal, October 18-20, 2023, Proceedings}, proceedings_a={GOODTECHS}, year={2024}, month={1}, keywords={natural language processing sentiment analysis global warming machine learning deep learning}, doi={10.1007/978-3-031-52524-7_4} }- Dragana Nikolova
Georgina Mircheva
Eftim Zdravevski
Year: 2024
Application of Traditional and Deep Learning Algorithms in Sentiment Analysis of Global Warming Tweets
GOODTECHS
Springer
DOI: 10.1007/978-3-031-52524-7_4
Abstract
The Earth’s surface is continuously warming, changing our planet’s average balance of nature. While we live and experience the impacts of global warming, people debate whether global warming is a threat to our planet or a hoax. This paper uses relevant global warming tweets to analyze sentiment and show how people’s opinions change over time concerning global warming. This analysis can contribute to understanding public perception, identify misinformation, and support climate advocacy. This paper proposes a data processing pipeline encompassing traditional and deep learning based methods, including VADER, TextBlob, Doc2Vec, Word2Vec, LSTMs, to name a few. The extensive testing shows that the combination of document embeddings and neural networks yields the best results of up to 97% AUC ROC and 93% accuracy. The findings enable the comprehension of human attitudes and actions related to this worldwide issue in production environments.
