Abstract
With the rapid expansion of digital newspapers, readers have an overwhelming amount of news available daily. However, it is difficult to keep track of the news that is only of interest to the reader. Because of this, this research discusses the use of deep learning for the classification of news, especially crime related, published by Mexican digital newspapers as well as an analysis of the predictions obtained through the proposed model. According to the experimental results, the proposed system achieves 98.87% of accuracy.
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Acknowledgment
This work is supported by the Sectoral Research Fund for Education with the CONACYT project 257357 and partially supported by the VIEP-BUAP project. The authors also would like to thank Universidad Autonoma Metropolitana, unit Azcapotzalco, with the research project SI001-18.
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Vidal, M.T., RodrÃguez, E.S., Reyes-Ortiz, J.A. (2020). Classification of Criminal News Over Time Using Bidirectional LSTM. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_61
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