UlyssesSD-Br: Stance Detection in Brazilian Political Polls

Political bill comments published in digital media may reveal the issuer's stances. Through this, we can identify and group the polarity of these public opinions. The automatic stance detection task involves viewing the text and the target topic. Due to the diversity and emergence of new bills, the challenge approached is to estimate the polarity of a new topic. Thus, this paper evaluates cross-target stance detection with many-to-one approaches in a collected Portuguese dataset of the political pool from the Brazilian Chamber of Deputies website. We proposed a new corpus for the bills' opinion domain and tested it in several models, where we achieved the best result with the mBERT model in classification with the joint input topic and comment method. We verify that the mBERT model successfully handled cross-target tasks with this corpus among the tested algorithms.