In the context of textual analysis, network-based procedures for topic detection are gaining attention as an alternative to classical topic models. Network-based procedures are based on the idea that documents can be represented as word co-occurrence networks, where topics are defined as groups of strongly connected words. Although many works have used network-based procedures for topic detection, there is a lack of systematic analysis of how different design choices, such as the building of the word co-occurrence matrix and the selection of the community detection algorithm, affect the final results in terms of detected topics. In this work, we present the results obtained by analysing a widely used corpus of news articles, showing how and to what extent the choices made during the design phase affect the results.
CITATION STYLE
Galluccio, C., Magnani, M., Vega, D., Ragozini, G., & Petrucci, A. (2023). Robustness and Sensitivity of Network-Based Topic Detection. In Studies in Computational Intelligence (Vol. 1078, pp. 259–270). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-21131-7_20
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