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.

这项研究介绍了主题度量，从提取的主题转换而来的虚拟变量，作为情绪度量的替代和补充，用于态度分类；通过使用 Bestvater 和 Monroe（2023）确定的三个数据集，本研究展示了 BERTopic 在提取连贯主题方面的熟练程度以及主题度量在态度分类中的有效性。实验结果表明，与传统方法（如 Dirichlet Allocation（LDA）和 Non-negative Matrix Factorization（NMF））相比，BERTopic 将连贯性得分提高了 17.07％至 54.20％，这些方法在早期政治学研究中很常见。此外，我们的结果表明，主题度量在态度分类中优于情绪度量，提高了高达 18.95％的性能。我们的发现表明，主题度量对于文本和语料库中态度和情绪相关性较弱的富有上下文的文本特别有效。情绪和主题度量的组合在大多数情况下可以实现最佳性能，并且可以进一步解决仅依赖情绪以及主题度量低一致性得分的局限性。