The article "An Ensemble Method for Radicalization and Hate Speech Detection Online Empowered by Sentic Computing" by Oscar Araque and Carlos A. Iglesias has been published in the Cognitive Computation journal (4.307 impact factor, JCR Q1 2019).

The paper can be found at this URL.


The dramatic growth of the Web has motivated researchers to extract knowledge from enormous repositories and to exploit the knowledge in myriad applications. In this study, we focus on natural language processing (NLP) and, more concretely, the emerging field of affective computing to explore the automation of understanding human emotions from texts. This paper continues previous efforts to utilize and adapt affective techniques into different areas to gain new insights. This paper proposes two novel feature extraction methods that use the previous sentic computing resources AffectiveSpace and SenticNet. These methods are efficient approaches for extracting affect-aware representations from text. In addition, this paper presents a machine learning framework using an ensemble of different features to improve the overall classification performance. Following the description of this approach, we also study the effects of known feature extraction methods such as TF-IDF and SIMilarity-based sentiment projectiON (SIMON). We perform a thorough evaluation of the proposed features across five different datasets that cover radicalization and hate speech detection tasks. To compare the different approaches fairly, we conducted a statistical test that ranks the studied methods. The obtained results indicate that combining affect-aware features with the studied textual representations effectively improves performance. We also propose a criterion considering both classification performance and computational complexity to select among the different methods.

The article "An Agent Based Simulation System for Analyzing Stress Regulation Policies at the Workplace" by Sergio Muñoz and Carlos A. Iglesias has been published in Journal of Computational Science, indexed by JCR Q1.

Abstract. Workplace stress has a significant impact on productivity, since keeping workers’ stress on an adequate level results a key factor for companies to increase their performance. While a high stress level may conduct to anxiety or absenteeism, a low level may also have undesirable consequences, such as lack of motivation. To identify and understand all the elements which interfere on workers’ stress results a key factor in order to improve workers’ performance. However, the complexity of human behavior increases the difficulty of recognizing the influence of these stressors and finding a way to regulate workers’ stress. This paper proposes the use of agent-based simulation techniques for addressing the challenge of analyzing workers’ behavior and stress regulation policies. The main contributions of the paper are: (i) the definition of a stress model that takes into account work and ambient conditions to calculate the stress and the productivity of workers; (ii) the implementation of this model in an agent-based simulation system, enabling the analysis of workplace stress and productivity for different stress regulation policies; (iii) the analysis of four different stress regulation policies; and (iv) the validation of the model with a sensitivity analysis and with its application to a living lab.

The article is available at:

Un grupo de investigadores de la Escuela Técnica Superior de Ingenieros de Telecomunicación (ETSIT) de la Universidad Politécnica de Madrid (UPM) ha desarrollado una plataforma interactiva que permite a los visitantes de los museos no sólo conocer más sobre las obras expuestas, sino que también ofrece la posibilidad de jugar a un juego de preguntas generadas automáticamente relacionadas con dichas obras. La plataforma se ha implementado en un escenario de la vida real y los buenos resultados obtenidos indican  que podría extrapolarse y adaptarse a otro tipo de museos o instituciones de patrimonio cultural.

La noticia se ha publicado en la portada de noticias la UPM.

El artículo del Grupo de Sistemas Inteligentes "Enhancing deep learning sentiment analysis with ensemble techniques in social applications" ha sido premiado bajo la categoría Artículo Científico más citado en su campo, bajo la resolución del día 15 de Diciembre el Rector de la Universidad Politécnica de Madrid, en los premios dentro de la Convocatoria Anual de Premios de Investigación e Innovación en el marco del Programa Propio de I+D+i 2020. Este artículo fue publicado en el año 2017 en la revista Expert Systems with Applications (JCR Q1 2017, 3.768), y forma parte de la tesis de Óscar Araque.

A día de hoy, esta publicación tiene:

  • 259 citas indexadas en Google Scholar
  • 178 citas indexadas en Scopus
  • 125 citas indexadas en Web of Science


The virtual kick-off of the H2020 project PARTICIPATION (Analyzing and Preventing Extremism Via Participation, SEP-210655026) has been held on 18th December 2021. 

The project PARTICIPATION aims at preventing extremism, radicalization, and polarisation that can lead to violence through more effective social and education policies and interventions that target at-risk groups to be performed through the establishment

of a holistic framework and the engagement\involvment of social actors, local communities, civil society, and policymakers.


UPM-GSI leads WP4, "Contrasting Radicalization, and Extremism via Communication", and its main role in the project is the development of an intelligent NLP engine for detecting the different stages of radicalization in youth. UPM also lead the development of open source methodological tools for evaluating counter and alternative narratives campaigns, so-called PTD (Prevention Tools Database). PTD will be integrated in the open information hub, a platform developed as part of the H2020 project TAKEDOWN, and currently being expanded in 2 ISFP projects (CHAMPIONs and ARMOUR).