  This study compares Term Frequency-Inverse Document Frequency (TF-IDF)
features with Sentence Transformers for detecting writers' stances--favorable,
opposing, or neutral--towards three significant topics: COVID-19 vaccine,
digital transformation, and women empowerment. Through empirical evaluation, we
demonstrate that Sentence Transformers outperform TF-IDF features across
various experimental setups. Our team, dzStance, participated in a stance
detection competition, achieving the 13th position (74.91%) among 15 teams in
Women Empowerment, 10th (73.43%) in COVID Vaccine, and 12th (66.97%) in Digital
Transformation. Overall, our team's performance ranked 13th (71.77%) among all
participants. Notably, our approach achieved promising F1-scores, highlighting
its effectiveness in identifying writers' stances on diverse topics. These
results underscore the potential of Sentence Transformers to enhance stance
detection models for addressing critical societal issues.
