# ClimateNLP: Analyzing Public Sentiment Towards Climate Change Using Natural Language Processing

arXiv: https://arxiv.org/abs/2310.08099 · HTML: https://mlsanity.com/paper/2310.08099/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 12 Oct 2023.

## Builds on (most cited first, 4 of 4)

- [ClimateBert: A Pretrained Language Model for Climate-Related Text](https://mlsanity.com/paper/2110.12010/) (2021)
- [ClimaText: A Dataset for Climate Change Topic Detection](https://mlsanity.com/paper/2012.00483/) (2020)
- [A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets](https://mlsanity.com/paper/2211.03533/) (2022)
- [CliMedBERT: A Pre-trained Language Model for Climate and Health-related Text](https://mlsanity.com/paper/2212.00689/) (2022)

## Built on by (most cited first, 5 of 5)

- [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://mlsanity.com/paper/2312.03014/) (2023)
- [WeatherQA: Can Multimodal Language Models Reason about Severe Weather?](https://mlsanity.com/paper/2406.11217/) (2024)
- [Analyzing Regional Impacts of Climate Change using Natural Language Processing Techniques](https://mlsanity.com/paper/2401.06817/) (2024)
- [Omni-Weather: A Unified Multimodal Model for Weather Radar Understanding and Generation](https://mlsanity.com/paper/2512.21643/) (2025)
- [Deciphering public attention to geoengineering and climate issues using machine learning and dynamic analysis](https://mlsanity.com/paper/2405.07010/) (2024)

## Abstract (arXiv)

Climate change's impact on human health poses unprecedented and diverse challenges. Unless proactive measures based on solid evidence are implemented, these threats will likely escalate and continue to endanger human well-being. The escalating advancements in information and communication technologies have facilitated the widespread availability and utilization of social media platforms. Individuals utilize platforms such as Twitter and Facebook to express their opinions, thoughts, and critiques on diverse subjects, encompassing the pressing issue of climate change. The proliferation of climate change-related content on social media necessitates comprehensive analysis to glean meaningful insights. This paper employs natural language processing (NLP) techniques to analyze climate change discourse and quantify the sentiment of climate change-related tweets. We use ClimateBERT, a pretrained model fine-tuned specifically for the climate change domain. The objective is to discern the sentiment individuals express and uncover patterns in public opinion concerning climate change. Analyzing tweet sentiments allows a deeper comprehension of public perceptions, concerns, and emotions about this critical global challenge. The findings from this experiment unearth valuable insights into public sentiment and the entities associated with climate change discourse. Policymakers, researchers, and organizations can leverage such analyses to understand public perceptions, identify influential actors, and devise informed strategies to address climate change challenges.