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arXiv · 2103.13550

Term-community-based topic detection with variable resolution

Andreas Hamm·Simon Odrowski
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Abstract

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.

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Andreas Hamm, Simon Odrowski. 2021-03-25. Term-community-based topic detection with variable resolution. https://doi.org/10.3390/info12060221

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