Term-community-based topic detection with variable resolution
This work addresses the need for interpretable topic detection for domain experts, though it is incremental as it builds on existing community detection methods.
The authors tackled topic detection in large text collections by introducing a network-based method with a resolution parameter to adjust topic granularity, and demonstrated its application on a news corpus with expert evaluations and comparisons to Latent Dirichlet Allocation.
Network-based procedures for topic detection in huge text collections offer an intuitive alternative to probabilistic topic models. We present in detail a method that is especially designed with the requirements of domain experts in mind. Like similar methods, it employs community detection in term co-occurrence graphs, but it is enhanced by including a resolution parameter that can be used for changing the targeted topic granularity. We also establish a term ranking and use semantic word-embedding for presenting term communities in a way that facilitates their interpretation. We demonstrate the application of our method with a widely used corpus of general news articles and show the results of detailed social-sciences expert evaluations of detected topics at various resolutions. A comparison with topics detected by Latent Dirichlet Allocation is also included. Finally, we discuss factors that influence topic interpretation.