Do you know what your senator advocates for in the committees they participate in? An LLM-based approach to topic and stance detection in parliamentary discussions

The legislative power in Brazil faces challenges in making discussions more accessible to the population, which is essential for strengthening democracy. Although stenographic notes of the Senate and House of Representatives committee meetings are publicly available, their length and volume make it impractical for citizens to follow what actually happens in those meetings. Therefore, a tool that can automatically extract useful and summarized information from these discussions would be transformative, empowering voters to monitor their representatives more effectively. This study investigates the efficacy of Large Language Models (LLMs) for detecting relevant topics and stances of parliamentarians. We conducted experiments using GPT-3.5-Turbo to interpret shorthand notes from the Federal Senate in 2023. The results were promising, with an average accuracy of 70% and 60% for topic and stance detection, respectively.