2026-04-27T19:55:00Zhttps://uvadoc.uva.es/oai/request
oai:uvadoc.uva.es:10324/651302024-01-29T20:03:01Zcom_10324_1191com_10324_931com_10324_894col_10324_1379
UVaDOC author Siddiqui, Hafeez Ur Rehman author Sainz de Abajo, Beatriz author Torre Díez, Isabel de la author Rustam, Furqan author Raza, Amjad author Atta, Sajjad author Ashraf, Imran 2024-01-27T19:54:43Z 2024-01-27T19:54:43Z 2023 PeerJ Computer Science, Enero 2023, vol. 9, p. e1134-e1158. 2376-5992 https://uvadoc.uva.es/handle/10324/65130 10.7717/peerj-cs.1134 e1134 e1158 PeerJ Computer Science 9 2376-5992 Se investiga la predicción del estado financiero de las empresas a partir del análisis de las emociones extraídas de las “llamadas de ganancias”, entendiendo por tales las conversaciones telefónicas entre la gerencia (de la empresa) y los medios de comunicación. Estas características extraídas de la red de memoria a corto plazo (long short-term memory network) se utilizan para entrenar los modelos de aprendizaje automático. Los resultados muestran que las funciones LSTM brindan un mejor rendimiento que el tradicional análisis de palabras junto con las funciones Term frequency–Inverse document frequency (TF-IDF). eng Attribution-NonCommercial-NoDerivatives 4.0 Internacional Predicting bankruptcy of firms using earnings call data and transfer learning info:eu-repo/semantics/article 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URL https://uvadoc.uva.es/bitstream/10324/65130/1/Published%20FINAL%20paper.pdf File MD5 6a6541feef34c0d11f8e3f1070d51576 8321704 application/pdf Published FINAL paper.pdf