ConTopic: Human-in-the-loop neural topic modeling with constraint loss for topic quality improvement

Existing neural topic models often produce semantically ambiguous or low-quality topics, limiting their effectiveness in real-world applications. To address this, we propose ConTopic , a human-in-the-loop framework that integrates user-defined “must-link” and “cannot-link” constraints to improve topic quality. Our method employs an autoencoder-based neural network to jointly embed words, documents, and topics into a unified semantic space, enabling constraint-guided optimization via a dedicated loss function. We also introduce an interactive editing tool with three visualization strategies that help users assess topic quality, explore semantic relations, and refine topics with minimal cognitive effort. Experiments on real-world datasets, supported by quantitative evaluations and user studies, confirm the effectiveness and usability of ConTopic in enhancing topic modeling workflows.