Similarity Fuzzy Semantic Network for Social Media Analysis Castro Peña, Juan Luis Francisco Aparicio, Manuel Interpretability Text mining Feature selection Microblogging Microblogging sites are being used as analysis avenues due to their peculiarities (promptness, short texts...). Lately, researchers have focused mainly in classification performance rather than interpretability. When the problem requires transparency, it is necessary to build interpretable pipelines, and even though, resulting models are too complex to be considered comprehensible, making it impossible for humans to understand the actual decisions. This paper presents a feature selection mechanism that is able to improve comprehensibility by using less but more meaningful features. Results show that our proposal is better and the most stable one in terms of accuracy, generalisation and comprehensibility in microblogging context. 2024-12-16T17:26:04Z 2024-12-16T17:26:04Z 2022-07 book part Castro, J.L., Francisco, M. (2022). Similarity Fuzzy Semantic Network for Social Media Analysis. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1601. Springer, Cham. https://doi.org/10.1007/978-3-031-08971-8_46 https://hdl.handle.net/10481/98066 10.1007/978-3-031-08971-8_46 eng open access Springer Nature