
Oscar AraqueUniversidad Politécnica de Madrid | UPM · Departamento de Ingeniería de Sistemas Telemáticos
Oscar Araque
Doctor of Engineering
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37
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Publications (37)
Nowadays, most health professionals use electronic health records to keep track of patients. To properly use and share these data, the community has relied on medical classification standards to represent patient information. However, the coding process is tedious and time-consuming, often limiting its application. This paper proposes a novel featu...
The moral value of liberty is a central concept in our inference system when it comes to taking a stance towards controversial social issues such as vaccine hesitancy, climate change, or the right to abortion. Here, we propose a novel Liberty lexicon evaluated on more than 3,000 manually annotated data both in in- and out-of-domain scenarios. As a...
Whether consciously or inadvertently, our messages can include toxic language which contributes to the polarization of social networks. Intelligent techniques can help us detect these expressions and even change them into kinder expressions by applying style transfer techniques. This work aims to advance detoxification style transfer techniques usi...
Depression is a growing issue in society's mental health that affects all areas of life and can even lead to suicide. Fortunately, prevention programs can be effective in its treatment. In this context, this work proposes an automatic system for detecting depression on social media based on machine learning and natural language processing methods....
After decades of improvements in the employment conditions of females in Spain, this process came to a sudden stop with the Great Spanish Recession of 2008. In this contribution, we analyse a large longitudinal corpus of national and regional news outlets employing advanced Natural Language Processing techniques to capture the valence of mentions o...
The popularity of current communication technologies has boosted the spread of polarization and radical ideologies, which can be exploited by terrorist organizations. Building upon previous research, this work focuses on the task of automatic radicalization detection in texts using natural language processing and machine learning techniques. In thi...
Quantifying the moral narratives expressed in the user-generated text, news, or public discourses is fundamental for understanding individuals' concerns and viewpoints and preventing violent protests and social polarisation. The Moral Foundation Theory (MFT) was developed to operationalise morality in a five-dimensional scale system. Recent develop...
Green areas play an important role in people’s well-being in urban areas. However, traditional survey methods hinder understanding their actual impact. Fortunately, social networking analysis provides valuable information that city planners can use to transform cities and improve city life. This research studies geolocated tweets published in parks...
Understanding radicalization pathways, drivers, and factors is essential for the effective design of prevention and counter-radicalization programs. Traditionally, the primary methods used by social scientists to detect these drivers and factors include literature reviews, qualitative interviews, focus groups, and quantitative methods based on surv...
Inside the NLP community there is a considerable amount of language resources created, annotated and released every day with the aim of studying specific linguistic phenomena. Despite a variety of attempts in order to organize such resources has been carried on, a lack of systematic methods and of possible interoperability between resources are sti...
GSITK is a framework to perform a wide variety of sentiment analysis tasks, including dataset acquisition, text preprocessing, model design, and performance evaluation. The framework is oriented to both researchers and practitioners, easing the replication of previous sentiment models, as well as offering implementations of common tasks. This is ac...
The analysis of the content of posts written on social media has established an important line of research in recent years. The study of these texts, as well as their relationship with each other and their dependence on the platform on which they are written, enables the behavior analysis of users and their opinions with respect to different domain...
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 text...
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 a...
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...
Moral rhetoric plays a fundamental role in how we perceive and interpret the information we receive, greatly influencing our decision-making process. Especially when it comes to controversial social and political issues, our opinions and attitudes are hardly ever based on evidence alone. The Moral Foundations Dictionary (MFD) was developed to opera...
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 th...
Several lexica for sentiment analysis have been developed; while most of these come with word polarity annotations (e.g., positive/negative), attempts at building lexica for finer-grained emotion analysis (e.g., happiness, sadness) have recently attracted significant attention. They are often exploited as a building block for developing emotion rec...
Opinions and attitudes towards controversial social and political issues are hardly ever based on evidence alone. Moral values play a fundamental role in the decision-making process of how we perceive and interpret information. The Moral Foundations Dictionary (MFD) was developed to operationalize moral values in text. In this study, we present Mor...
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. Pre...
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 cooperatin...
Lexical resources are widely popular in the field of Sentiment Analysis, as they represent a resource that directly encodes sentimental knowledge. Usually sentiment lexica are used for polarity estimation through the matching of words contained in a text and their associated lexicon sentiment polarities. Nevertheless, such resources have limitation...
Several lexica for sentiment analysis have been developed and made available in the NLP community. While most of these come with word polarity annotations (e.g. positive/negative), attempts at building lexica for finer-grained emotion analysis (e.g. happiness, sadness) have recently attracted significant attention. Such lexica are often exploited a...
Deep learning techniques for Sentiment Analysis have become very popular. They provide automatic feature extraction and both richer representation capabilities and better performance than traditional feature based techniques (i.e., surface methods). Traditional surface approaches are based on complex manually extracted features, and this extraction...
The evolution of the Internet of Things leads to new opportunities for the contemporary notion of smart offices, where employees can benefit from automation to maximize their productivity and performance. However, although extensive research has been dedicated to analyze the impact of workers’ emotions on their job performance, there is still a lac...
Deep learning techniques for Sentiment Analysis have become very popular. They provide automatic feature extraction and both richer representation capabilities and better performance than traditional feature based techniques (i.e., surface methods). Traditional surface approaches are based on complex manually extracted features, and this extraction...