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In the last ten years, artificial intelligence (AI) methods and techniques have witnessed great advances and they already make part of the usual landscape from where new or old problems are tackled in different areas of human knowledge. Three particular aspects are behind this leap forward: a generalized availability and variety of data; a deeper understanding of the mathematics governing the underlying algorithmics; and hardware capabilities allowing wide and deep experimental pipelines over data. The main challenge in each problem and context of application now lies on understanding how can these technologies can be used, their reach and limitations so that the can be aligned with the aims of each specific problem at hand. Social and communication sciences are no exception, but show particularities that define which ai technologies and methods are most appropriate (i.e. natural language processing). This works presents an introduction to the methodology under which ai models are built, to potentially useful ai services in the field and, finally, some examples of applications illustrating practical and technical considerations in this respect.

Raul Ramos Pollán, Universidad de Antioquia

Profesor Asociado, Facultad de Ingeniería
Ramos Pollán, R. (2020). Perspectives and Challenges of Artificial Intelligence Techniques in the Field of Social Sciences and Communication. Anuario Electrónico De Estudios En Comunicación Social "Disertaciones", 13(1), 21–34. https://doi.org/10.12804/revistas.urosario.edu.co/disertaciones/a.7774

Alaei, 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

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