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.
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Acknowledgements
This article is based upon work from COST Action CA19121 (Good Brother: Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living), supported by COST (European Cooperation in Science and Technology). COST is a funding agency for research and innovation networks. COST Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career, and innovation. More information at https://www.cost.eu.
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Nikolova, D., Mircheva, G., Zdravevski, E. (2024). Application of Traditional and Deep Learning Algorithms in Sentiment Analysis of Global Warming Tweets. In: Coelho, P.J., Pires, I.M., Lopes, N.V. (eds) Smart Objects and Technologies for Social Good. GOODTECHS 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 556. Springer, Cham. https://doi.org/10.1007/978-3-031-52524-7_4
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