Beyond Sentiment: Leveraging Topic Metrics for Political Stance
  Classification
Weihong Qi
Sentiment analysis, widely critiqued for capturing merely the overall tone of
a corpus, falls short in accurately reflecting the latent structures and
political stances within texts. This study introduces topic metrics, dummy
variables converted from extracted topics, as both an alternative and
complement to sentiment metrics in stance classification. By employing three
datasets identified by Bestvater and Monroe (2023), this study demonstrates
BERTopic's proficiency in extracting coherent topics and the effectiveness of
topic metrics in stance classification. The experiment results show that
BERTopic improves coherence scores by 17.07% to 54.20% when compared to
traditional approaches such as Dirichlet Allocation (LDA) and Non-negative
Matrix Factorization (NMF), prevalent in earlier political science research.
Additionally, our results indicate topic metrics outperform sentiment metrics
in stance classification, increasing performance by as much as 18.95%. Our
findings suggest topic metrics are especially effective for context-rich texts
and corpus where stance and sentiment correlations are weak. The combination of
sentiment and topic metrics achieve an optimal performance in most of the
scenarios and can further address the limitations of relying solely on
sentiment as well as the low coherence score of topic metrics.