ClimateNLP: Analyzing Public Sentiment Towards Climate Change: Conclusions and References

Table of Links

• Abstract & Introduction
• Related Studies
• Materials and Methods
• Proposed Approach
• Results and Discussions
• Conclusions and References

6 Conclusions

Climate change, a pressing global concern, necessitates thorough analysis and understanding across diverse domains to mitigate its impacts effectively. In recent years, the fusion of NLP techniques and machine learning algorithms has emerged as a promising approach for comprehending the complexities and nuances of climate change through the lens of textual data. This paper utilized the advancements in domain-specific large language models to harness the potential of NLP in addressing the challenges posed by climate change through sentiment analysis. By leveraging advanced NLP methodologies, we could identify climate change discourse that may enable uncovering valuable insights and facilitate informed decision-making.

References

VS Anoop. Sentiment classification of diabetes-related tweets using transformer-based deep learning approach. In International Conference on Advances in Computing and Data Sciences, pages 203–214. Springer, 2023.

VS Anoop and S Sreelakshmi. Public discourse and sentiment during mpox outbreak: an analysis using natural language processing. Public Health, 218:114–120, 2023.

VS Anoop, Jose Thekkiniath, and Usharani Hareesh Govindarajan. We chased covid-19; did we forget measles?-public discourse and sentiment analysis on spiking measles cases using natural language processing. In International Conference on Multi-disciplinary Trends in Artificial Intelligence, pages 147–158. Springer, 2023.

Sina Ardabili, Amir Mosavi, Majid Dehghani, and Annamária R Várkonyi-Kóczy. Deep learning and machine learning in hydrological processes climate change and earth systems a systematic review. In Engineering for Sustainable Future: Selected papers of the 18th International Conference on Global Research and Education Inter-Academia– 2019 18, pages 52–62. Springer, 2020.

Ceylan Ceylan. Application of Natural Language Processing to Unstructured Data: A Case Study of Climate Change. PhD thesis, Massachusetts Institute of Technology, 2022.

B Jalalzadeh Fard, SA Hasan, and JE Bell. Climedbert: A pre-trained language model for climate and health-related text. arXiv preprint arXiv:2212.00689, 2022.

Muhammad Khurram Iqbal, Kamran Abid, Salah u din Ayubi, Naeem Aslam, et al. Omicron tweet sentiment analysis using ensemble learning. Journal of Computing & Biomedical Informatics, 4(02):160–171, 2023.

Sarin Jickson, VS Anoop, and S Asharaf. Machine learning approaches for detecting signs of depression from social media. In Proceedings of International Conference on Information Technology and Applications: ICITA 2022, pages 201–214. Springer, 2023.

Romieo John, VS Anoop, and S Asharaf. Health mention classification from user-generated reviews using machine learning techniques. In Proceedings of International Conference on Information Technology and Applications: ICITA 2022, pages 175–188. Springer, 2023.

S Lekshmi and VS Anoop. Sentiment analysis on covid-19 news videos using machine learning techniques. In Proceedings of International Conference on Frontiers in Computing and Systems: COMSYS 2021, pages 551–560. Springer, 2022.

Mustapha Lydiri, Yousef El Mourabit, Youssef El Habouz, and Mohamed Fakir. A performant deep learning model for sentiment analysis of climate change. Social Network Analysis and Mining, 13(1):8, 2022.

Nabila Mohamad Sham and Azlinah Mohamed. Climate change sentiment analysis using lexicon, machine learning and hybrid approaches. Sustainability, 14(8):4723, 2022.

Uddagiri Sirisha and Sai Chandana Bolem. Aspect based sentiment & emotion analysis with roberta, lstm. International Journal of Advanced Computer Science and Applications, 13(11), 2022.

Apoorva Upadhyaya, Marco Fisichella, and Wolfgang Nejdl. A multi-task model for sentiment aided stance detection of climate change tweets, 2022.

Milan Varghese and VS Anoop. Deep learning-based sentiment analysis on covid-19 news videos. In Proceedings of International Conference on Information Technology and Applications: ICITA 2021, pages 229–238. Springer, 2022.

Francesco S Varini, Jordan Boyd-Graber, Massimiliano Ciaramita, and Markus Leippold. Climatext: A dataset for climate change topic detection. arXiv preprint arXiv:2012.00483, 2020.

Nicolas Webersinke, Mathias Kraus, Julia Anna Bingler, and Markus Leippold. Climatebert: A pretrained language model for climate-related text. arXiv preprint arXiv:2110.12010, 2021.