SOURCE-LINKED INTELLIGENCE

Does Syntax Matter? A Graph-Augmented Variational Topic Model for Computational Social Sciences

arXiv · AI, language, vision and robotics · article · Sep 7, 2026 · UTC

Topic modeling is widely used in computational social sciences to identify latent themes in large text corpora. Traditional approaches rely on Bag-of-Words representations and generative models such as LDA, while recent methods like BERTopic operate on dense document embeddings. This paper introduces the Structural Contextual Probabilistic Topic Model (SCPTM), an architecture that incorporates syntactic dependency relations into topic inference. SCPTM represents a corpus as a heterogeneous graph of documents and words connected by lexical and syntactic edges, processed through a Graph Attentio

Read original source ↗ Open in workspace

recordType

paper

region

Global