- Data, Statistics and AI for Well-Being of People and Organisations
- A cura di Luigi Fabbris, Alberto Franci, Pietro Renzi
Public perception of sustainable technologies: ABSA on Reddit discussions
- Luisa Stracqualursi
- © 2026 Author(s) |
- CC BY 4.0
- DOI: 10.36253/979-12-215-1062-1.69
The transition toward sustainable technologies is a central topic in public discourse, with diverse opinions emerging on their effectiveness, adoption challenges, and societal impact. This study employs Aspect-Based Sentiment Analysis (ABSA) using PyABSA, a BERT-based NLP framework, to analyze sentiment towards green innovations and renewable energy technologies. The dataset consists of user-generated discussions from Reddit, specifically from the communities r/futurology, r/sustainability, and r/technology, collected in February 2024. Using PRAW (Python Reddit API Wrapper), I extracted textual data from Reddit posts and comments, applying preprocessing and aspect-based sentiment classification. Sentiment is categorized as positive, neutral, or negative, allowing for fine-grained insights into public perceptions. This study demonstrates how AI-driven sentiment analysis can provide valuable insights into public attitudes toward sustainability, supporting better decision-making in climate tech adoption and environmental policy development. By leveraging advanced NLP techniques, this research contributes to the growing intersection of artificial intelligence, sustainability, and public discourse analysis.
- Keywords:
- Gender inequalities,
- Survey,
- Citizens’ perceptions,
Alma Mater Studiorum University of Bologna, Italy - ORCID: 0000-0002-8022-1377
- Devlin, J., Chang, M.W., Lee, K., Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT, pp. 4171–4186.
- Fischer, D., Stanszus, L., Geiger, S.M., Grossman, P., Schrader, U. (2020). Mindfulness and sustainable consumption: A systematic literature review of research approaches and findings. Journal of Cleaner Production, 243, 118544. DOI: 10.1016/j.jclepro.2017.06.007
- IPCC. (2021). Climate Change 2021: The Physical Science Basis. Cambridge University Press. DOI: 10.3410/f.740620545.793587812
- Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), pp. 1-167. San Rafael, CA: Morgan & Claypool Publishers. DOI: 10.1007/978-3-031-02145-9
- Pang, B., Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), pp. 1–135. DOI: 10.1561/9781601981516
- Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., … Chintala, S. (2019). PyTorch: An imperative style, high-performance deep learning library. In Proceedings of the 33rd International Conference on Neural Information Processing Systems (NeurIPS), pp. 8024–8035.
- Reddit API Documentation. (2023). Retrieved from https://www.reddit.com/dev/api. Last access: 28/02/2025.
- Schouten, K., Frasincar, F. (2016). Survey on aspect-level sentiment analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), pp. 813–830. DOI: 10.1109/tkde.2015.2485209
- Sovacool, B.K., Axsen, J. (2018). Functional, symbolic and societal frames for automobility: Implications for sustainability transitions. Transportation Research Part A: Policy and Practice, 118, pp. 730–746. DOI: 10.1016/j.tra.2018.10.008
- Sun, C., Huang, L., Qiu, X. (2019). Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT), pp. 180–185. DOI: 10.18653/v1/d19-5505
- Tang, D., Qin, B., Liu, T. (2016). Aspect level sentiment classification with deep memory network. EMNLP, pp. 214–224. DOI: 10.18653/v1/d16-1021
- Yang, H., Chen, D. (2022). PyABSA: Open Framework for Aspect-Based Sentiment Analysis. GitHub Repository. https://github.com/yangheng95/PyABSA. Last access: on 30/01/2025. DOI: 10.1145/3583780.3614752
Informazioni sul capitolo
Titolo del capitolo
Public perception of sustainable technologies: ABSA on Reddit discussions
Autori
Luisa Stracqualursi
Lingua
Inglese
DOI
10.36253/979-12-215-1062-1.69
Opera sottoposta a peer review
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Informazioni bibliografiche
Titolo del libro
Data, Statistics and AI for Well-Being of People and Organisations
Curatori
Luigi Fabbris, Alberto Franci, Pietro Renzi
Opera sottoposta a peer review
Numero di pagine
420
Anno di pubblicazione
2026
Copyright
© 2026 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
Firenze University Press
DOI
10.36253/979-12-215-1062-1
ISBN Print
979-12-215-1061-4
eISBN (pdf)
979-12-215-1062-1
eISBN (xml)
979-12-215-1063-8
Collana
Applied Statistics Series