Text analysis methods based on word co-occurrence have yielded useful results in humanities and social sciences research. Whereas these methods provide a useful overview of a corpus, they cannot determine the predicates relating co-occurring elements with each other. For instance, if France and the phrase "binding commitments" co-occur within a sentence, how are both elements related? Is France in favour of, or against binding commitments? Different natural language processing (NLP) technologies can identify related elements in text, and the predicates relating them. We are developing a workflow to analyze the Earth Negotiations Bulletin, which summarizes international climate negotiations. A sentence in this corpus can contain several verbal or nominal predicates indicating support and opposition. Results were uneven when applying Open Relation Extraction tools to this corpus. To address these challenges, we developed a workflow with a domain model, and analysis rules that exploit annotations for semantic roles and pronominal anaphora, provided by an NLP pipeline.