Similarity Fuzzy Semantic Network for Social Media Analysis

Castro Peña, Juan Luis; Francisco Aparicio, Manuel

doi:10.1007/978-3-031-08971-8_46

Manuscrito Aceptado (294.3Kb)

Identificadores

URI: https://hdl.handle.net/10481/98066

DOI: 10.1007/978-3-031-08971-8_46

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Editorial

Springer Nature

Materia

Interpretability

Text mining

Feature selection

Microblogging

Fecha

2022-07

Referencia bibliográfica

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

Patrocinador

Spanish Ministry of Economy and Competitiveness (MINECO), project FFI2016-79748-R; European Social Fund (ESF); FPI 2017 predoctoral programme, Spanish Ministry of Economy and Competitiveness (MINECO), grant reference BES-2017-081202

Resumen

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.

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