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.