Emotional language is central to how environmental issues are communicated and received by the public. To better understand how such language is interpreted, we conducted an annotation study on sentiment and emotional language in texts from the environmental activist group Extinction Rebellion. The annotation process revealed substantial disagreement among annotators, highlighting the complexity and subjectivity involved in interpreting emotional language. In this paper, we analyze the sources of these disagreements, offering insights into how individual perspectives shape annotation outcomes. Our work contributes to ongoing discussions on perspectivism in NLP and emphasizes the importance of human-centered approaches and citizen science in analyzing environmental communication.

This project employs state-of-the-art Natural Language Processing (NLP) techniques to analyze the online communication of international Environmental Movement Organizations (EMOs). First, we introduce our overall EMO dataset and describe it through topic modeling. Second, we evaluate current sentiment and emotion classification models for our specific dataset. Third, as we are currently in our annotation process, we evaluate our current progress and issues to determine the most effective approach for creating a high-quality annotated dataset that captures the nuances of EMO communication. Finally, we emphasize the need for domain-specific datasets and tailored NLP tools and suggest refinements for our annotation process moving forward.