Perspectivas y retos de las técnicas de inteligencia artificial en el ámbito de las ciencias sociales y de la comunicación
Barra lateral del artículo
Contenido principal del artículo
Descargas
Raul Ramos Pollán, Universidad de Antioquia
Profesor Asociado, Facultad de IngenieríaAlaei, A., Becken, S., & Stantic, B. (2019). Sentiment analysis in tourism: capitalizing on big data. Journal of
Travel Research, 58(2) , 175-191. Doi: https://doi.org/10.1177/0047287517747753
Arcila-Calderón, C., Ortega-Mohedano, F., Jiménez-Amores, J., & Trullenque, S. (2017). Análisis supervisado
de sentimientos políticos en español: clasificación en tiempo real de tweets basada en aprendizaje automá-
tico. El profesional de la información, 26(5), 1699-2407. Doi: https://doi.org/10.3145/epi.2017.sep.18
Bachhety, S., et al. (2018). Crime Detection Using Text Recognition and Face Recognition. International Journal of Pure and Applied Mathematics, 119(15), 2797-2807. Recuperado de https://acadpubl.eu/hub/2018-
-15/2/298.pdf
Chang, Y., Yi Lee, F. & Chen, C. (2018). A public opinion keyword vector for social sentiment analysis
research. En Tenth International Conference on Advanced Computational Intelligence (ICACI). IEEE .
Diou, C., Lelekas, P . & Delopoulos, A. (2018). Image-Based Surrogates of Socio-Economic Status in Urban
Neighborhoods Using Deep Multiple Instance Learning. Journal of Imaging 4(11), 125. Doi: 10.3390/
jimaging4110125
Etter, M., et al. (2018). Measuring O rganizational Legitimacy in Social Media: Assessing Citizens’ Judgments
with Sentiment Analysis. Business & Society, 57(1), 60-97. Doi: 10.1177/0007650316683926
Gómez-Torres, E., Jaimes, R., Hidalgo, O. & Luján-Mora, S. (2018). Influence of social networks on the
analysis of sentiment applied to the political situation in Ecuador. Enfoque UTE, 9(1), 67- 78. Doi: 10.29019/
enfoqueute.v9n1.235
Ma, T. (2018). Multi-Level Relationships between Satellite-Derived Nighttime Lighting Signals and Social
Media–Derived Human Population Dynamics. Remote Sensing, 10(7), 1128. Doi: 10.3390/rs10071128
Martínez-Cámara, E., Díaz-Galiano, M. C., García-Cumbreras, A., García-Vega, M. & Villena-Román, J. (2017).
Resumen de TASS 2017. TASS 2017: Workshop on Semantic Analysis at SEPLN Proceedings ( 13- 21). Recuperado
de http://www.sepln.org/workshops/tass/
Moor J. (2006). The Dartmouth College Artificial Intelligence Conference: The Next Fifty Years. AI Magazine,
(4), 87- 91. Doi: 10.1609/aimag.v27i4.1911
Olazaran, M. (1996). A Sociological Study of the Official History of the Perceptrons Controversy. Social
Studies of Science, 26(3), 611- 659. Doi: 10.1177/030631296026003005
Pranav, A., Sukiennik, N. & Hui, P. (2018). Inflo: News Categorization and Keyphrase Extraction for Implementation in an Aggregation System. arXiv preprint arXiv:1812.03781
Poecze, F. , Ebster, C. & Strauss, C. (2018). Social media metrics and sentiment analysis to evaluate the effectiveness of social media posts. Procedia Computer Science, 130, 660-666. Doi: https://doi.org/10.1016/j.
procs.2018.04.117
Rosenblatt, F. (1957). The Perceptron--a perceiving and recognizing automaton. Report. Cornell Aeronautical
Laboratory, 85-460-1.
Russakovsky, O. et al. (2015). ImageNet Large Scale Visual Recognition Challenge. IJCV, 115(3), 211-252.
Doi: https://doi.org/10.1007/s11263-015-0816-y
Stegmeier, J., et al. (2019). Multi-method Discourse Analysis of Twitter Communication: A Comparison of
Two Global Political Issues. En Scholz R. (eds.) Quantifying Approaches to Discourse for Social Scientists
(pp. 285-314). Postdisciplinary Studies in Discourse. Palgrave Macmillan, Cham .
Thelwall, M. (2018). Gender bias in sentiment analysis. Online Information Review, 42(1), 45-57. Doi: 0.1108/
OIR-05-2017-0139
Detalles del artículo

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.