Abstract
In this work, we present a crawler that collects news articles and a classifier that identifies the section to which these articles belong. Due to a large number of available sources of information, a tool for gathering and filtering news articles about specific interests is necessary. For instance, a person might be interested in news about sports or science, and it could be necessary to check several websites to obtain this kind of news finally. Therefore, in this work, we propose a web application that uses a crawler to collect news articles from different websites automatically, then a classifier determines the section of each news article, and finally, the news articles that match the section of interest are displayed in the web application.
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For instance, the Economy section is called Finanzas (finance) in some websites.
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Acknowledgments
No acknowledgments given We thank the support of Insituto Politécnico Nacional (IPN), ESCOM-IPN, SIP-IPN projects numbers: SIP-20220620, SIP-2083, SIP-20220925 COFAA-IPN, EDI-IPN and CONACyT-SNI.
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García-Mendoza, CV., Juárez Gambino, O. (2022). Web Crawler and Classifier for News Articles. In: Pichardo Lagunas, O., Martínez-Miranda, J., Martínez Seis, B. (eds) Advances in Computational Intelligence. MICAI 2022. Lecture Notes in Computer Science(), vol 13613. Springer, Cham. https://doi.org/10.1007/978-3-031-19496-2_10
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