Artificial Intelligence in the Social Sciences
Approach from Sentiment Analysis
DOI:
https://doi.org/10.48162/rev.48.097Keywords:
Artificial intelligence, Sentiment Analysis, Social Sciences, Vector Support MachinesAbstract
Artificial Intelligence (AI) has developed as a fundamental ally in the Social Sciences, transforming their practice. This article alludes to the importance of AI in the Social Sciences and is exemplified by the task of researching digitized discourses. Sentiment analysis is presented, which can be carried out with written discourses and/or visual discourses on social networks. One of the most characteristic mathematical models of sentiment analysis is addressed: support vector machines (SVM), and an algorithm is programmed in Python to interpret a short text using an SVM. The main objective of the work is to be an easy reference resource for scientists interested in the use of AI as a methodological tool in the Social Sciences.
References
Bahit. E. (2012). Curso: Python para principiantes. https://archive.org/details/2012CursoPythonParaPrincipiantes
Chen, X. y He, K. (2020) Exploring Simple Siamese Representation Learning. https://arxiv.org/pdf/2011.10566.pdf
Cheng, J. Dong, L., Lapata, M. (2016) Long Short-Term Memory-Networks for Machine Reading. https://arxiv.org/pdf/1601.06733.pdf
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y. (2014) Generative Adversarial Networks. https://arxiv.org/pdf/1406.2661.pdf
Gugerty, L. (2006). Newell and Simon’s Logic Theorist: Historical Background and Impact on Cognitive Modeling. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 50(9), pp. 880-884. https://doi.org/10.1177/154193120605000904
Guenther, N. y Schonlau, M. (2016) Support vector machines. The Stata Journal 16, Number 4, pp. 917–937.
Gurney, K. (2007). Neural Networks for Perceptual Processing: From Simulation Tools to Theories. Philosophical Transactions: Biological Sciences, 362(1479), pp. 339–353. http://www.jstor.org/stable/20209847
Kaplan, J. (2017) Inteligencia Artificial: Lo que todo el mundo debe saber. España: Teell Editorial.
Kress, G. y van Leeuwen, T. (2021) Reading Imagens: The grammar of visual design. New York: Routledge.
Michelucci, U. (2022) An Introduction to Autoencoders. https://arxiv.org/pdf/2201.03898.pdf
Moguerza, J. M., y Muñoz, A. (2006). Support Vector Machines with Applications. Statistical Science, 21(3), pp. 322–336. http://www.jstor.org/stable/27645765
O'Shea, K. y Nash, R. (2015) An Introduction to Convolutional Neural Networks. https://arxiv.org/pdf/1511.08458.pdf
Schmidt, R.M. (2019) Recurrent Neural Networks (RNNs): A gentle Introduction and Overview. https://arxiv.org/pdf/1912.05911.pdf
Sukhbaatar, S., Szlam, A., Weston, J., Fergus, R. (2015) End-To-End Memory Networks. https://arxiv.org/pdf/1503.08895.pdf
Turner, R.E. (2023) An Introduction to Transformers. https://arxiv.org/pdf/2304.10557.pdf
Vorobioff, J., Cerrotta, S. Morel, N.E. y Amadio, A. (2022) Inteligencia Artificial y Redes Neuronales: Fundamentos, Ejercicios y Aplicaciones con Python y Matlab. Buenos Aires: Editorial de la Universidad Tecnológica Nacional.
Wightman, R., Touvron, H., Jégou, H. (2021) ResNet strikes back: An improved training procedure in timm. https://arxiv.org/pdf/2110.00476.pdf
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Copyright (c) 2025 Antonio Aguilera
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