@inproceedings{liebeskind-lewandowska-tomaszczyk-2025-climate,
    title = "Climate Change Discourse Over Time: A Topic-Sentiment Perspective",
    author = "Liebeskind, Chaya  and
      Lewandowska-Tomaszczyk, Barbara",
    editor = "Cardoso, Henrique Lopes  and
      Sousa-Silva, Rui  and
      Koponen, Maarit  and
      Pareja-Lora, Antonio",
    booktitle = "Proceedings of the 2nd LUHME Workshop",
    month = oct,
    year = "2025",
    address = "Bologna, Italy",
    publisher = "LUHME",
    url = "https://preview.aclanthology.org/ingest-luhme/2025.luhme-1.11/",
    pages = "109--116",
    abstract = "The present paper focuses on the study of opinion dynamics and opinion shifts in social media in the context of climate change discourse in terms of the quantitative NLP analysis, supported by a linguistic outlook. The research draws on two comparable collections of climate-related social media data from different time periods, each based on trending climate-related hashtags and annotated for relevant sentiment values. The quantitative computer-based research methodology has been supported by a language-based perspective in the pragma-linguistic form. The research shows that the latter data source, for the majority of identified topics, exhibits a significant reduction in negative sentiment and a dominance of positive sentiment, i.e., a potential temporal evolution in public sentiment toward climate change. To achieve this, we used a BERT-based clustering approach to identify dominant themes within a combined dataset of tweets from both periods. Subsequently, a unified sentiment classification framework using a Large Language Model (LLM) was applied to reclassify all tweets, ensuring consistent and climate-specific sentiment analysis across both datasets. This methodology allowed for a coherent comparison of public attitudes and their evolution in different time periods and thematic structures."
}