RWS Project
I contributed to a large-scale project focused on political discourse analysis, where the goal was to train models to detect sentiment, bias, stance, and topic relevance in political texts. My responsibilities included labeling social media posts, news excerpts, and political speeches for emotional tone, ideological slant, and factual content. The project involved tens of thousands of samples and required strict adherence to quality control measures, including inter-annotator agreement thresholds and periodic calibration tasks. I maintained high accuracy and consistency while working with nuanced and often subjective political content, helping ensure the reliability of downstream NLP applications.