Valencia García, Rafael

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Valencia García, Rafael

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Valencia García, Rafael

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Universidad de Murcia. Departamento de Informática y Sistemas

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Open Access
Fine grain emotion analysis in Spanish using linguistic features and transformers
(PeerJ, 2024-04-30) Salmerón Ríos, Alejandro; García Díaz, José Antonio; Pan, Ronghao; Valencia García, Rafael; Informática y Sistemas; Facultades de la UMU::Facultad de Informática
Mental health issues are a global concern, with a particular focus on the rise of depression. Depression affects millions of people worldwide and is a leading cause of suicide, particularly among young people. Recent surveys indicate an increase in cases of depression during the COVID-19 pandemic, which affected approximately 5.4% of the population in Spain in 2020. Social media platforms such as X (formerly Twitter) have become important hubs for health information as more people turn to these platforms to share their struggles and seek emotional support. Researchers have discovered a link between emotions and mental illnesses such as depression. This correlation provides a valuable opportunity for automated analysis of social media data to detect changes in mental health status that might otherwise go unnoticed, thus preventing more serious health consequences. Therefore, this research explores the field of emotion analysis in Spanish towards mental disorders. There are two contributions in this area. On the one hand, the compilation, translation, evaluation and correction of a novel dataset composed of a mixture of other existing datasets in the bibliography. This dataset compares a total of 16 emotions, with an emphasis on negative emotions. On the other hand, the in-depth evaluation of this novel dataset with several state-ofthe- art transformers based on encoder-only and encoder-decoder architectures. The analysis compromises monolingual, multilingual and distilled models as well as feature integration techniques. The best results are obtained with the encoder-only MarIA model, with a macro-average F1 score of 60.4771%.
Open Access
Smart analysis of economics sentiment in Spanish based on linguistic features and transformers
(IEEE, 2023-02-10) García Díaz, José Antonio; García-Sánchez, Francisco ; Valencia García, Rafael; Informática y Sistemas; Facultad de Informática
Texts related to economics and finances are characterized by the use of words and expressions whose meaning (and the sentiments they convey) substantially depend on the context. This poses a major challenge to Natural Language Processing tasks in general, and Sentiment Analysis in particular. For lowresource languages such as Spanish, this situation becomes even more acute. Yet, the latest advancements in the field, including word embeddings and transformers, have allowed to boost the performance of Sentiment Analysis solutions. In this work we explore the impact of the combination of different feature sets in the accuracy of Sentiment Analysis in Spanish financial texts. For this, a corpus with 15,915 tweets has been compiled and manually annotated as either positive, negative, or neutral. Then, feature sets based on contextual and non-contextual embeddings along with linguistic features were evaluated both individually and combined. The best results, with a weighted F1-score of 73.15880%, were obtained with a combination of feature sets by means of knowledge integration
Open Access
Hope speech detection in Spanish. The LGBT case
(Springer, 2023-03-17) García‑Baena, Daniel; García‑Cumbreras, Miguel Ángel; Jiménez‑Zafra, Salud María; García Díaz, José Antonio; Valencia García, Rafael; Informática y Sistemas; Facultad de Informática
In recent years, systems have been developed to monitor online content and remove abusive, offensive or hateful content. Comments in online social media have been analyzed to find and stop the spread of negativity using methods such as hate speech detection, identification of offensive language or detection of abusive language. We define hope speech as the type of speech that is able to relax a hostile environment and that helps, gives suggestions and inspires for good to a number of people when they are in times of illness, stress, loneliness or depression. Detecting it automatically, in order to give greater diffusion to positive comments, can have a very significant effect when it comes to fighting against sexual or racial discrimination or when we intend to foster less bellicose environments. In this article we perform a complete study on hope speech in Spanish, analyzing existing solutions and available resources. In addition, we have generated a quality resource, a new Twitter dataset on LGBT community, and we have conducted some experiments that can serve as a baseline for further research.
Open Access
Compilation and evaluation of the Spanish SatiCorpus 2021 for satire identification using linguistic features and transformers
(Springer , 2021-12-17) García Díaz, José Antonio; Valencia García, Rafael; Informática y Sistemas; Facultades de la UMU::Facultad de Informática
Open Access
Evaluation of transformer models for financial targeted sentiment analysis in Spanish
(PeerJ, 2023-05-09) Pan, Ronghao; García Díaz, José Antonio; García Sánchez, Francisco; Valencia García, Rafael; Informática y Sistemas; Facultades de la UMU::Facultad de Informática
Open Access
UMUCorpusClassifier: compilation and evaluation of linguistic corpus for Natural Language Processing tasks
(Sociedad Española de Procesamiento del Lenguaje Natural, 2020) Almela, Ángela; García Díaz, José Antonio; Alcaraz Marmol, Gema; Valencia García, Rafael; Filología Inglesa
The development of an annotated corpus is a very time-consuming task. Although some researchers have proposed the automatic annotation of a corpus based on ad-hoc heuristics, valid hypotheses cannot always be made. Even when the annotation process is performed by human annotators, the quality of the corpus is heavily in uenced by disagreements between annotators or with themselves. Therefore, the lack of supervision of the annotation process can lead to poor quality corpus. In this work, we propose a demonstration of UMUCorpusClassi er, a NLP tool for aid researches for compiling corpus as well as coordinating and supervising the annotation process. This tool eases the daily supervision process and permits to detect deviations and inconsistencies during early stages of the annotation process.
Open Access
Seeing through deception: a computational approach to deceit detection in written communication
(Association for Computational Linguistics, 2012) Almela, Ángela; Valencia García, Rafael; Cantos Gómez, Pascual; Filología Inglesa; Informática y Sistemas
The present paper addresses the question of the nature of deception language. Specifically, the main aim of this piece of research is the exploration of deceit in Spanish written communication. We have designed an automatic classifier based on Support Vector Machines (SVM) for the identification of deception in an ad hoc opinion corpus. In order to test the effectiveness of the LIWC2001 categories in Spanish, we have drawn a comparison with a Bag-of-Words (BoW) model. The results indicate that the classification of the texts is more successful by means of our initial set of variables than with the latter system. These findings are potentially applicable to areas such as forensic linguistics and opinion mining, where extensive research on languages other than English isneeded.
Open Access
Smart recommender for the configuration of software project development teams
(Elsevier, 2024-12-15) Rodríguez García, Miguel Ángel; García Sánchez, Francisco; Valencia García, Rafael; Informática y Sistemas; Facultades de la UMU::Facultad de Informática
The development of Social Media has caused an incredible change in the way people communicate and share information. It provides a set of platforms, web-based applications and services that facilitate the collaborative creation of content and the sharing of ideas and interests. Since its inception, Social Media technologies have been increasingly used in different fields that have integrated them into their daily lives. In Software Engineering, for example, it has caused a disruptive change in the software development model, changing the way that the projects are approached by promoting collaborative environments. This effect has led to the proliferation of the software development communities where huge amounts of information are published every day. Therefore, when a project is started and a development team needs to be assembled, it is difficult to select and identify the most suitable developer profiles for such a project by considering all the disseminated information. To solve this problem, we have proposed an ontology-based system to help find a suitable group of developers to develop a project. The system uses web services to extract user profiles from GitHub, and semantic technologies to represent and annotate the features of the extracted data. Then, when the system receives the natural language description of the project to be developed, it identifies and extracts relevant concepts such as technologies, platforms, tools, among others. As a result, it analyzes the extracted information and lists the most suitable developers to assemble a team of developers with the right technical skills to tackle the software project. For evaluation purposes, we generated a random list of GitHub profiles, and collected a corpus of documents describing research projects and patents. The system produced very promising results, achieving a MAP@5 and F-Measure of 0.68.
Open Access
Psychographic traits identification based on political ideology: an author analysis study on Spanish politicians’ tweets posted in 2020
(Elsevier, 2022-05) García Díaz, José Antonio; Colomo Palacios, Ricardo; Valencia García, Rafael; Informática y Sistemas; Facultades de la UMU::Facultad de Informática
In general, people are usually more reluctant to follow advice and directions from politicians who do not have their ideology. In extreme cases, people can be heavily biased in favour of a political party at the same time that they are in sharp disagreement with others, which may lead to irrational decision making and can put people’s lives at risk by ignoring certain recommendations from the authorities. Therefore, considering political ideology as a psychographic trait can improve political micro-targeting by helping public authorities and local governments to adopt better communication policies during crises. In this work, we explore the reliability of determining psychographic traits concerning political ideology. Our contribution is twofold. On the one hand, we release the PoliCorpus-2020, a dataset composed by Spanish politicians’ tweets posted in 2020. On the other hand, we conduct two authorship analysis tasks with the aforementioned dataset: an author profiling task to extract demographic and psychographic traits, and an authorship attribution task to determine the author of an anonymous text in the political domain. Both experiments are evaluated with several neural network architectures grounded on explainable linguistic features, statistical features, and state-of-the-art transformers. In addition, we test whether the neural network models can be transferred to detect the political ideology of citizens. Our results indicate that the linguistic features are good indicators for identifying fine-grained political affiliation, they boost the performance of neural network models when combined with embedding-based features, and they preserve relevant information when the models are tested with ordinary citizens. Besides, we found that lexical and morphosyntactic features are more effective on author profiling, whereas stylometric features are more effective in authorship attribution.
Open Access
A study on LIWC categories for opinion mining in Spanish reviews
(SAGE Publications, 2014-08-26) Salas Zárate, María del Pilar; López López, Estanislao; Valencia García, Rafael; Aussenac Gilles, Natalie; Almela, Ángela; Alor Hernández, Giner; Filología Inglesa
With the exponential growth of social media, that is, blogs and social networks, organizations and individual persons are increasingly using the number of reviews of these media for decision-making about a product or service. Opinion mining detects whether the emotions of an opinion expressed by a user on Web platforms in natural language are positive or negative. This paper presents extensive experiments to study the effectiveness of the classification of Spanish opinions in five categories: highly positive, highly negative, positive, negative and neutral, using the combination of the psychological and linguistic features of LIWC (Linguistic Inquiry and Word Count). LIWC is a text analysis software that enables the extraction of different psychological and linguistic features from natural language text. For this study, two corpora have been used, one about movies and one about technological products. Furthermore, we conducted a comparative assessment of the performance of various classification techniques, J48, SMO and BayesNet, using precision, recall and F-measure metrics. The findings revealed that the positive and negative categories provide better results than the other categories. Finally, experiments on both corpora indicated that SMO produces better results than BayesNet and J48 algorithms, obtaining an F-measure of 90.4 and 87.2% in each domain.