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Dr. Carlos A. Iglesias
Universidad Politécnica de Madrid

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0 Affective Computing
0 Natural Language Processing
0 Social Computing
0 machine learning
0 multiagent systems

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machine learning
multiagent systems
Natural Language Processing
Affective Computing

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Article
Published: 16 February 2021 in Cognitive Computation
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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.

ACS Style

Oscar Araque; Carlos A. Iglesias. An Ensemble Method for Radicalization and Hate Speech Detection Online Empowered by Sentic Computing. Cognitive Computation 2021, 1 -14.

AMA Style

Oscar Araque, Carlos A. Iglesias. An Ensemble Method for Radicalization and Hate Speech Detection Online Empowered by Sentic Computing. Cognitive Computation. 2021; ():1-14.

Chicago/Turabian Style

Oscar Araque; Carlos A. Iglesias. 2021. "An Ensemble Method for Radicalization and Hate Speech Detection Online Empowered by Sentic Computing." Cognitive Computation , no. : 1-14.

Journal article
Published: 11 February 2021 in Journal of Computational Science
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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.

ACS Style

Sergio Muñoz; Carlos A. Iglesias. An agent based simulation system for analyzing stress regulation policies at the workplace. Journal of Computational Science 2021, 51, 101326 .

AMA Style

Sergio Muñoz, Carlos A. Iglesias. An agent based simulation system for analyzing stress regulation policies at the workplace. Journal of Computational Science. 2021; 51 ():101326.

Chicago/Turabian Style

Sergio Muñoz; Carlos A. Iglesias. 2021. "An agent based simulation system for analyzing stress regulation policies at the workplace." Journal of Computational Science 51, no. : 101326.

Journal article
Published: 04 December 2020 in Information
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Nowadays, we are witnessing a shift in the way emergencies are being managed. On the one hand, the availability of big data and the evolution of geographical information systems make it possible to manage and process large quantities of information that can hugely improve the decision-making process. On the other hand, digital humanitarianism has shown to be very beneficial for providing support during emergencies. Despite this, the full potential of combining automatic big data processing and digital humanitarianism approaches has not been fully realized, though there is an initial body of research. This paper aims to provide a reference architecture for emergency management that instantiates the NIST Big Data Reference Architecture to provide a common language and enable the comparison of solutions for solving similar problems.

ACS Style

Carlos Iglesias; Alfredo Favenza; Álvaro Carrera. A Big Data Reference Architecture for Emergency Management. Information 2020, 11, 569 .

AMA Style

Carlos Iglesias, Alfredo Favenza, Álvaro Carrera. A Big Data Reference Architecture for Emergency Management. Information. 2020; 11 (12):569.

Chicago/Turabian Style

Carlos Iglesias; Alfredo Favenza; Álvaro Carrera. 2020. "A Big Data Reference Architecture for Emergency Management." Information 11, no. 12: 569.

Journal article
Published: 18 September 2020 in IEEE Access
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The application of natural language to improve students’ interaction with information systems is demonstrated to be beneficial. In particular, advances in cognitive computing enable a new way of interaction that accelerates insight from existing information sources, thereby contributing to the process of learning. This work aims at researching the application of cognitive computing in blended learning environments. We propose a modular cognitive agent architecture for pedagogical question answering, featuring social dialogue (small talk), improved for a specific knowledge domain. This system has been implemented as a personal agent to assist students in learning Data Science and Machine Learning techniques. Its implementation includes the training of machine learning models and natural language understanding algorithms in a human-like interface. The effectiveness of the system has been validated through an experiment.

ACS Style

Daniel Carlander-Reuterfelt; Alvaro Carrera; Carlos A. Iglesias; Oscar Araque; Juan Fernando Sanchez Sanchez Rada; Sergio Munoz. JAICOB: A Data Science Chatbot. IEEE Access 2020, 8, 180672 -180680.

AMA Style

Daniel Carlander-Reuterfelt, Alvaro Carrera, Carlos A. Iglesias, Oscar Araque, Juan Fernando Sanchez Sanchez Rada, Sergio Munoz. JAICOB: A Data Science Chatbot. IEEE Access. 2020; 8 (99):180672-180680.

Chicago/Turabian Style

Daniel Carlander-Reuterfelt; Alvaro Carrera; Carlos A. Iglesias; Oscar Araque; Juan Fernando Sanchez Sanchez Rada; Sergio Munoz. 2020. "JAICOB: A Data Science Chatbot." IEEE Access 8, no. 99: 180672-180680.

Journal article
Published: 05 August 2020 in Applied Sciences
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Museums play a crucial role in preserving cultural heritage. However, the forms in which they display cultural heritage might not be the most effective at piquing visitors’ interest. Therefore, museums tend to integrate different technologies that aim to create engaging and memorable experiences. In this context, the emerging Internet of Things (IoT) technology results particularly promising due to the possibility of implementing smart objects in museums, granting exhibits advanced interaction capabilities. Gamification techniques are also a powerful technique to draw visitors’ attention. These often rely on interactive question-based games. A drawback of such games is that questions must be periodically regenerated, and this is a time-consuming task. To confront these challenges, this paper proposes a low-maintenance gamified smart object platform that automates the creation of questions by exploiting semantic web technologies. The platform has been implemented in a real-life scenario. The results obtained encourage the use of the platform in the museum considered. Therefore, it appears to be a promising work that could be extrapolated and adapted to other kinds of museums or cultural heritage institutions.

ACS Style

Alejandro López-Martínez; Álvaro Carrera; Carlos A. Iglesias. Empowering Museum Experiences Applying Gamification Techniques Based on Linked Data and Smart Objects. Applied Sciences 2020, 10, 5419 .

AMA Style

Alejandro López-Martínez, Álvaro Carrera, Carlos A. Iglesias. Empowering Museum Experiences Applying Gamification Techniques Based on Linked Data and Smart Objects. Applied Sciences. 2020; 10 (16):5419.

Chicago/Turabian Style

Alejandro López-Martínez; Álvaro Carrera; Carlos A. Iglesias. 2020. "Empowering Museum Experiences Applying Gamification Techniques Based on Linked Data and Smart Objects." Applied Sciences 10, no. 16: 5419.

Journal article
Published: 25 July 2020 in Electronics
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E-learning has become a critical factor in the academic environment due to the endless number of possibilities that it opens for the learning context. However, these platforms often suppose to increase the difficulties for the communication between teachers and students. Without having real contact between teachers and students, the former finds it harder to adapt their methods and content to their students, while the students also find complications for maintaining their focus. This paper aims to address this challenge with the use of emotion and engagement recognition techniques. We propose an emotion-aware e-learning platform architecture that recognizes students’ emotions and attention in order to improve their academic performance. The system integrates a semantic task automation system that allows users to easily create and configure their own automation rules to adapt the study environment. The main contributions of this paper are: (1) the design of an emotion-aware learning analytics architecture; (2) the integration of this architecture in a semantic task automation platform; and (3) the validation of the use of emotion recognition in the e-learning platform using partial least squares structural equation modeling (PLS-SEM) methodology.

ACS Style

Sergio Muñoz; Enrique Sánchez; Carlos A. Iglesias. An Emotion-Aware Learning Analytics System Based on Semantic Task Automation. Electronics 2020, 9, 1194 .

AMA Style

Sergio Muñoz, Enrique Sánchez, Carlos A. Iglesias. An Emotion-Aware Learning Analytics System Based on Semantic Task Automation. Electronics. 2020; 9 (8):1194.

Chicago/Turabian Style

Sergio Muñoz; Enrique Sánchez; Carlos A. Iglesias. 2020. "An Emotion-Aware Learning Analytics System Based on Semantic Task Automation." Electronics 9, no. 8: 1194.

Conference paper
Published: 15 June 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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Bike-sharing systems (BSS) have been implemented in numerous cities around the world to reduce the traffic generated by motorized vehicles, due to the benefits they bring to the city, such as reducing congestion or decreasing pollution generation. Caused by their impact on urban mobility, the research community has increased their interest in their study, trying to understand user behavior and improving the user experience. This paper has the goal of analyzing the impact of different policies of incentives on the user experience and their impact on the BSS service. An agent-based simulation model has been developed using data collected from the BSS service of Madrid, so-called BiciMad. Route generation has been calculated based o n OpenStreetMaps. The system has been evaluated, analyzing the results generated on different incentive policies. The main conclusion is that variable incentives outperform the current incentive policy of the service. Finally, a sensitivity analysis is presented to validate the proper variability of results for the model parameters.

ACS Style

Alberto López Santiago; Carlos A. Iglesias; Álvaro Carrera. Improving Sustainable Mobility with a Variable Incentive Model for Bike-Sharing Systems Based on Agent-Based Social Simulation. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 158 -170.

AMA Style

Alberto López Santiago, Carlos A. Iglesias, Álvaro Carrera. Improving Sustainable Mobility with a Variable Incentive Model for Bike-Sharing Systems Based on Agent-Based Social Simulation. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():158-170.

Chicago/Turabian Style

Alberto López Santiago; Carlos A. Iglesias; Álvaro Carrera. 2020. "Improving Sustainable Mobility with a Variable Incentive Model for Bike-Sharing Systems Based on Agent-Based Social Simulation." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 158-170.

Conference paper
Published: 15 June 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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Bike-Sharing Systems (BSSs) have been implemented in numerous cities around the world to reduce the traffic generated by motorized vehicles, due to the benefits they bring to the city, such as reducing congestion or decreasing pollution generation. Caused by their impact on urban mobility, the research community has increased their interest in their study, trying to understand user behavior and improving the user experience. This demonstration shows the simulator developed to analyze the impact of a variable incentive model for BSSs based on Agent-based Social Simulation. The model has been developed using data collected directly from BiciMad, the BSS of the city of Madrid, Spain. The developed simulator uses OpenStreetMaps as a route generator software. The simulated scenario for this demonstration consists of a 7-day series of simulations with different traffic flows to observe the impact of different policies according to different traffic intensity.

ACS Style

Alberto López Santiago; Carlos A. Iglesias; Álvaro Carrera. A Practical Demonstration of a Variable Incentive Model for Bike-Sharing Systems Based on Agent-Based Social Simulation. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 443 -446.

AMA Style

Alberto López Santiago, Carlos A. Iglesias, Álvaro Carrera. A Practical Demonstration of a Variable Incentive Model for Bike-Sharing Systems Based on Agent-Based Social Simulation. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():443-446.

Chicago/Turabian Style

Alberto López Santiago; Carlos A. Iglesias; Álvaro Carrera. 2020. "A Practical Demonstration of a Variable Incentive Model for Bike-Sharing Systems Based on Agent-Based Social Simulation." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 443-446.

Journal article
Published: 21 April 2020 in Applied Sciences
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In recent years, the sharing economy has become popular, with outstanding examples such as Airbnb, Uber, or BlaBlaCar, to name a few. In the sharing economy, users provide goods and services in a peer-to-peer scheme and expose themselves to material and personal risks. Thus, an essential component of its success is its capability to build trust among strangers. This goal is achieved usually by creating reputation systems where users rate each other after each transaction. Nevertheless, these systems present challenges such as the lack of information about new users or the reliability of peer ratings. However, users leave their digital footprints on many social networks. These social footprints are used for inferring personal information (e.g., personality and consumer habits) and social behaviors (e.g., flu propagation). This article proposes to advance the state of the art on reputation systems by researching how digital footprints coming from social networks can be used to predict future behaviors on sharing economy platforms. In particular, we have focused on predicting the reputation of users in the second-hand market Wallapop based solely on their users’ Twitter profiles. The main contributions of this research are twofold: (a) a reputation prediction model based on social data; and (b) an anonymized dataset of paired users in the sharing economy site Wallapop and Twitter, which has been collected using the user self-mentioning strategy.

ACS Style

Antonio Prada; Carlos A. Iglesias. Predicting Reputation in the Sharing Economy with Twitter Social Data. Applied Sciences 2020, 10, 2881 .

AMA Style

Antonio Prada, Carlos A. Iglesias. Predicting Reputation in the Sharing Economy with Twitter Social Data. Applied Sciences. 2020; 10 (8):2881.

Chicago/Turabian Style

Antonio Prada; Carlos A. Iglesias. 2020. "Predicting Reputation in the Sharing Economy with Twitter Social Data." Applied Sciences 10, no. 8: 2881.

Journal article
Published: 01 March 2020 in Applied Sciences
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Recent works have shown that sentiment analysis on social media can be improved by fusing text with social context information. Social context is information such as relationships between users and interactions of users with content. Although existing works have already exploited the networked structure of social context by using graphical models or techniques such as label propagation, more advanced techniques from social network analysis remain unexplored. Our hypothesis is that these techniques can help reveal underlying features that could help with the analysis. In this work, we present a sentiment classification model (CRANK) that leverages community partitions to improve both user and content classification. We evaluated this model on existing datasets and compared it to other approaches.

ACS Style

J. Fernando Sánchez-Rada; Carlos A. Iglesias. CRANK: A Hybrid Model for User and Content Sentiment Classification Using Social Context and Community Detection. Applied Sciences 2020, 10, 1662 .

AMA Style

J. Fernando Sánchez-Rada, Carlos A. Iglesias. CRANK: A Hybrid Model for User and Content Sentiment Classification Using Social Context and Community Detection. Applied Sciences. 2020; 10 (5):1662.

Chicago/Turabian Style

J. Fernando Sánchez-Rada; Carlos A. Iglesias. 2020. "CRANK: A Hybrid Model for User and Content Sentiment Classification Using Social Context and Community Detection." Applied Sciences 10, no. 5: 1662.

Journal article
Published: 17 January 2020 in IEEE Access
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The Internet has become an important tool for modern terrorist groups as a means of spreading their propaganda messages and recruitment purposes. Previous studies have shown that the analysis of social signs can help in the analysis, detection, and prediction of radical users. In this work, we focus on the analysis of affect signs in social media and social networks, which has not been yet previously addressed. The article contributions are: (i) a novel dataset to be used in radicalization detection works, (ii) a method for utilizing an emotion lexicon for radicalization detection, and (iii) an application to the radical detection domain of an embedding-based semantic similarity model. Results show that emotion can be a reliable indicator of radicalization, as well as that the proposed feature extraction methods can yield high-performance scores.

ACS Style

Oscar Araque; Carlos A. Iglesias. An Approach for Radicalization Detection Based on Emotion Signals and Semantic Similarity. IEEE Access 2020, 8, 17877 -17891.

AMA Style

Oscar Araque, Carlos A. Iglesias. An Approach for Radicalization Detection Based on Emotion Signals and Semantic Similarity. IEEE Access. 2020; 8 (99):17877-17891.

Chicago/Turabian Style

Oscar Araque; Carlos A. Iglesias. 2020. "An Approach for Radicalization Detection Based on Emotion Signals and Semantic Similarity." IEEE Access 8, no. 99: 17877-17891.

Editorial
Published: 22 November 2019 in Applied Sciences
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Sentiment analysis has become a key technology to gain insight from social networks. The field has reached a level of maturity that paves the way for its exploitation in many different fields such as marketing, health, banking or politics. The latest technological advancements, such as deep learning techniques, have solved some of the traditional challenges in the area caused by the scarcity of lexical resources. In this Special Issue, different approaches that advance this discipline are presented. The contributed articles belong to two broad groups: technological contributions and applications.

ACS Style

Carlos A. Iglesias; Antonio Moreno. Sentiment Analysis for Social Media. Applied Sciences 2019, 9, 5037 .

AMA Style

Carlos A. Iglesias, Antonio Moreno. Sentiment Analysis for Social Media. Applied Sciences. 2019; 9 (23):5037.

Chicago/Turabian Style

Carlos A. Iglesias; Antonio Moreno. 2019. "Sentiment Analysis for Social Media." Applied Sciences 9, no. 23: 5037.

Original software publication
Published: 07 November 2019 in Knowledge-Based Systems
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Senpy is a framework to develop, evaluate and publish web services for sentiment and emotion analysis in text. The framework is aimed towards both developers and users. For developers, it is a means to evaluate their classifiers and easily publish them as web services. For users, it is a way to consume sentiment analysis from different providers through the same interface. This is achieved through a combination of an API aligned with the NLP Interchange Format (NIF) service specification, the use of semantic formats and a series of well established vocabularies. The framework is Open Source, and has been used extensively in several projects. As a result, several Senpy Open Source services are available for use and download.

ACS Style

J. Fernando Sánchez-Rada; Oscar Araque; Carlos A. Iglesias. Senpy: A framework for semantic sentiment and emotion analysis services. Knowledge-Based Systems 2019, 190, 105193 .

AMA Style

J. Fernando Sánchez-Rada, Oscar Araque, Carlos A. Iglesias. Senpy: A framework for semantic sentiment and emotion analysis services. Knowledge-Based Systems. 2019; 190 ():105193.

Chicago/Turabian Style

J. Fernando Sánchez-Rada; Oscar Araque; Carlos A. Iglesias. 2019. "Senpy: A framework for semantic sentiment and emotion analysis services." Knowledge-Based Systems 190, no. : 105193.

Journal article
Published: 03 August 2019 in Sensors
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Traditionally, fault diagnosis in telecommunication network management is carried out by humans who use software support systems. The phenomenal growth in telecommunication networks has nonetheless triggered the interest in more autonomous approaches, capable of coping with emergent challenges such as the need to diagnose faults’ root causes under uncertainty in geographically-distributed environments, with restrictions on data privacy. In this paper, we present a framework for distributed fault diagnosis under uncertainty based on an argumentative framework for multi-agent systems. In our approach, agents collaborate to reach conclusions by arguing in unpredictable scenarios. The observations collected from the network are used to infer possible fault root causes using Bayesian networks as causal models for the diagnosis process. Hypotheses about those fault root causes are discussed by agents in an argumentative dialogue to achieve a reliable conclusion. During that dialogue, agents handle the uncertainty of the diagnosis process, taking care of keeping data privacy among them. The proposed approach is compared against existing alternatives using benchmark multi-domain datasets. Moreover, we include data collected from a previous fault diagnosis system running in a telecommunication network for one and a half years. Results show that the proposed approach is suitable for the motivational scenario.

ACS Style

Álvaro Carrera; Eduardo Alonso; Carlos A. Iglesias. A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks. Sensors 2019, 19, 3408 .

AMA Style

Álvaro Carrera, Eduardo Alonso, Carlos A. Iglesias. A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks. Sensors. 2019; 19 (15):3408.

Chicago/Turabian Style

Álvaro Carrera; Eduardo Alonso; Carlos A. Iglesias. 2019. "A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks." Sensors 19, no. 15: 3408.

Conference paper
Published: 14 July 2019 in Algorithms and Data Structures
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This work presents an agent-based model of radicalization growth based on social theories. The model aims at improving the understanding of the influence of social links on radicalism spread. The model consists of two main entities, a Network Model and an Agent Model. The Network Model updates the agent relationships based on proximity and homophily; it simulates information diffusion and updates the agents’ beliefs. The model has been evaluated and implemented in Python with the agent-based social simulator Soil. In addition, it has been evaluated through sensitivity analysis.

ACS Style

Tasio Méndez; J. Fernando Sánchez-Rada; Carlos A. Iglesias; Paul Cummings. Analyzing Radicalism Spread Using Agent-Based Social Simulation. Algorithms and Data Structures 2019, 263 -282.

AMA Style

Tasio Méndez, J. Fernando Sánchez-Rada, Carlos A. Iglesias, Paul Cummings. Analyzing Radicalism Spread Using Agent-Based Social Simulation. Algorithms and Data Structures. 2019; ():263-282.

Chicago/Turabian Style

Tasio Méndez; J. Fernando Sánchez-Rada; Carlos A. Iglesias; Paul Cummings. 2019. "Analyzing Radicalism Spread Using Agent-Based Social Simulation." Algorithms and Data Structures , no. : 263-282.

Journal article
Published: 13 May 2019 in Information Fusion
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Sentiment analysis in social media is harder than in other types of text due to limitations such as abbreviations, jargon, and references to existing content or concepts. Nevertheless, social media provides more information beyond text, such as linked media, user reactions, and relations between users. We refer to this information as social context. Recent works have successfully leveraged the fusion of text with social context for sentiment analysis tasks. However, these works are usually limited to specific aspects of social context, and there have not been any attempts to analyze and apply social context systematically. This work aims to bridge this gap by providing three main contributions: 1) a formal definition of social context; 2) a framework for classifying and comparing approaches that use social context; 3) a review of existing works based on the defined framework.

ACS Style

J. Fernando Sánchez-Rada; Carlos A. Iglesias. Social context in sentiment analysis: Formal definition, overview of current trends and framework for comparison. Information Fusion 2019, 52, 344 -356.

AMA Style

J. Fernando Sánchez-Rada, Carlos A. Iglesias. Social context in sentiment analysis: Formal definition, overview of current trends and framework for comparison. Information Fusion. 2019; 52 ():344-356.

Chicago/Turabian Style

J. Fernando Sánchez-Rada; Carlos A. Iglesias. 2019. "Social context in sentiment analysis: Formal definition, overview of current trends and framework for comparison." Information Fusion 52, no. : 344-356.

Special issue article
Published: 03 May 2019 in Transactions on Emerging Telecommunications Technologies
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Fault Management is a vital issue for any network operator since the beginning of the telecommunications era. As networks have become more and more complex, their management systems are crucial for any operator company. In this ecosystem, the Software‐Defined Networking (SDN) approach has appeared as a possible solution for different networking issues. The flexibility provided by SDN to the network management enables a great dynamism in the configuration of network devices. However, this feature introduces the cost of a potential increase in failures because every modification introduced on the control plane is a new possibility for failures to appear and cause a decrement of the quality for offered services. Because of the growing pace of the networks, the classical approach is not feasible to cope that dynamism. Increasing the number of human operators in charge of the fault management process would increase the operation cost dramatically. Thus, this paper presents an approach to apply machine learning over a big data framework for an autonomous fault management process in SDN networks. In this paper, we present a Semantic Data Lake framework for a self‐diagnosis service, which is deployed on top of an SDN management platform. Moreover, we have developed a prototype of the proposed service with different diagnosis models for SDN networks. Models and algorithms have been evaluated showing good results.

ACS Style

Fernando Benayas; Álvaro Carrera; Manuel García‐Amado; Carlos A. Iglesias. A semantic data lake framework for autonomous fault management in SDN environments. Transactions on Emerging Telecommunications Technologies 2019, 30, e3629 .

AMA Style

Fernando Benayas, Álvaro Carrera, Manuel García‐Amado, Carlos A. Iglesias. A semantic data lake framework for autonomous fault management in SDN environments. Transactions on Emerging Telecommunications Technologies. 2019; 30 (9):e3629.

Chicago/Turabian Style

Fernando Benayas; Álvaro Carrera; Manuel García‐Amado; Carlos A. Iglesias. 2019. "A semantic data lake framework for autonomous fault management in SDN environments." Transactions on Emerging Telecommunications Technologies 30, no. 9: e3629.

Journal article
Published: 16 March 2019 in Information Processing & Management
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The impact of online reviews on businesses has grown significantly during last years, being crucial to determine business success in a wide array of sectors, ranging from restaurants, hotels to e-commerce. Unfortunately, some users use unethical means to improve their online reputation by writing fake reviews of their businesses or competitors. Previous research has addressed fake review detection in a number of domains, such as product or business reviews in restaurants and hotels. However, in spite of its economical interest, the domain of consumer electronics businesses has not yet been thoroughly studied. This article proposes a feature framework for detecting fake reviews that has been evaluated in the consumer electronics domain. The contributions are fourfold: (i) Construction of a dataset for classifying fake reviews in the consumer electronics domain in four different cities based on scraping techniques; (ii) definition of a feature framework for fake review detection; (iii) development of a fake review classification method based on the proposed framework and (iv) evaluation and analysis of the results for each of the cities under study. We have reached an 82% F-Score on the classification task and the Ada Boost classifier has been proven to be the best one by statistical means according to the Friedman test.

ACS Style

Rodrigo Barbado; Oscar Araque; Carlos A. Iglesias. A framework for fake review detection in online consumer electronics retailers. Information Processing & Management 2019, 56, 1234 -1244.

AMA Style

Rodrigo Barbado, Oscar Araque, Carlos A. Iglesias. A framework for fake review detection in online consumer electronics retailers. Information Processing & Management. 2019; 56 (4):1234-1244.

Chicago/Turabian Style

Rodrigo Barbado; Oscar Araque; Carlos A. Iglesias. 2019. "A framework for fake review detection in online consumer electronics retailers." Information Processing & Management 56, no. 4: 1234-1244.

Journal article
Published: 01 February 2019 in Knowledge-Based Systems
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ACS Style

Oscar Araque; Ganggao Zhu; Carlos A. Iglesias. A semantic similarity-based perspective of affect lexicons for sentiment analysis. Knowledge-Based Systems 2019, 165, 346 -359.

AMA Style

Oscar Araque, Ganggao Zhu, Carlos A. Iglesias. A semantic similarity-based perspective of affect lexicons for sentiment analysis. Knowledge-Based Systems. 2019; 165 ():346-359.

Chicago/Turabian Style

Oscar Araque; Ganggao Zhu; Carlos A. Iglesias. 2019. "A semantic similarity-based perspective of affect lexicons for sentiment analysis." Knowledge-Based Systems 165, no. : 346-359.

Journal article
Published: 25 January 2019 in Revista Internacional de Tecnologías en la Educación
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This work proposesan approach that combines teaching general concepts in a technology-agnostic fashion with a cooperative learning approach oriented to a the resolution of a challenge in a competitive environment. In this way, students both learn the theory and then put in practice these concepts in class, exploring different options and cooperating in smalls groups. Such groups compete between them through in order to obtain the better solution. Our experience applying this approach in the classroom have been successful. Student satisfaction, test performance, and student understanding are high.RESUMENEste trabajo propone un enfoque al aprendizaje de Big Data, que combina los conceptos generales de una manera agnóstica a la tecnología, y la puesta en práctica de estos conceptos mediante aprendizaje cooperativo orientado a la resolución de un reto en un entorno competitivo. De esta manera, los alumnos aprenden los conceptos teóricos y los ponen en práctica explorando diferentes opciones y cooperando en grupos. Estos grupos compiten entre sí para obtener la mejor solución. Nuestra experiencia aplicando este enfoque ha sido un éxito.La satisfacción de los estudiantes, el rendimiento y la comprensión de los conceptos son altos.

ACS Style

Juan Fernando Sánchez-Rada; Oscar Araque; Álvaro Carrera Barroso; Carlos Ángel Iglesias Fernández. Enseñando Big Data con Lápiz, Papel y Tijeras / Teaching Big Data With Pen, Paper and Scissors. Revista Internacional de Tecnologías en la Educación 2019, 5, 63 -68.

AMA Style

Juan Fernando Sánchez-Rada, Oscar Araque, Álvaro Carrera Barroso, Carlos Ángel Iglesias Fernández. Enseñando Big Data con Lápiz, Papel y Tijeras / Teaching Big Data With Pen, Paper and Scissors. Revista Internacional de Tecnologías en la Educación. 2019; 5 (2):63-68.

Chicago/Turabian Style

Juan Fernando Sánchez-Rada; Oscar Araque; Álvaro Carrera Barroso; Carlos Ángel Iglesias Fernández. 2019. "Enseñando Big Data con Lápiz, Papel y Tijeras / Teaching Big Data With Pen, Paper and Scissors." Revista Internacional de Tecnologías en la Educación 5, no. 2: 63-68.