提出SCA模型，让每篇文档可发现多个主题，适合大规模文本分析。
Semantic Component Analysis: Introducing Multi-Topic Distributions to Clustering-Based Topic Modeling
- 基于聚类框架引入分解步骤，实现每样本多主题建模。
- 在多语言数据上主题数量翻倍，噪声率接近零，主题质量与BERTopic相当。
- 计算成本相近时优于TopicGPT，适合资源有限的大规模应用。
主题建模是文本分析的关键方法，但现有方法难以扩展到大规模数据集，或受限于每文档仅一个主题的假设。为此，我们提出语义成分分析（Semantic Component Analysis, SCA），通过在基于聚类的主题建模框架中引入分解步骤，实现每样本发现多个主题。我们在英文、豪萨语和中文的Twitter数据集上评估SCA，结果表明其主题一致性与多样性与BERTopic相当，同时主题数量至少翻倍，噪声率接近零。在相似计算预算下，SCA还优于基于大模型的TopicGPT。因此，SCA为大规模数据的主题建模提供了一种高效有效的解决方案。
原文摘要 · Abstract (English)
Topic modeling is a key method in text analysis, but existing approaches fail to efficiently scale to large datasets or are limited by assuming one topic per document. Overcoming these limitations, we introduce Semantic Component Analysis (SCA), a topic modeling technique that discovers multiple topics per sample by introducing a decomposition step to the clustering-based topic modeling framework. We evaluate SCA on Twitter datasets in English, Hausa and Chinese. There, it achieves competitive coherence and diversity compared to BERTopic, while uncovering at least double the topics and maintaining a noise rate close to zero. We also find that SCA outperforms the LLM-based TopicGPT in scenarios with similar compute budgets. SCA thus provides an effective and efficient approach for topic modeling of large datasets.
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