Examinando por Autor "Arenas-Parra, Mar"

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A comprehensive framework for explainable cluster analysis
(Elsevier, 2024-03) Alvarez-Garcia, Miguel; Ibar-Alonso, Raquel; Arenas-Parra, Mar
Machine learning has proven to be a powerful tool for knowledge extraction from large data sets across different domains. Data quality and results interpretability are essential when applying machine learning to inform decision-making processes. This is especially true for clustering methods, which are frequently employed for extracting knowledge from large data sets, due to their unsupervised nature. Although there are significant recent developments in explainable artificial intelligence (XAI) applied to unsupervised problems, they focus primarily on cluster interpretability and often overlook data quality challenges. Moreover, these developments are typically designed to use specific clustering algorithms, limiting their adaptability to incorporate alternative techniques. We propose a novel and comprehensive four-step sequential framework for explainable cluster analysis on high-dimensional mixed-type data to address these limitations. The framework encompasses data preprocessing, dimensionality reduction, clustering, and classification to ensure robust and explainable results. The proposed methodology has also been implemented in an open-source Python package called Clust-learn, designed to be accessible and customizable for researchers and practitioners. The framework has been validated by applying a case study focusing on large-scale assessments in education, effectively illustrating the strength and usefulness of the methodology in extracting and synthesizing knowledge from complex real-world data.
Opinion mining of green energy sentiment: a Russia-Ukraine conflict analysis
(MDPI, 2022-07-21) Ibar-Alonso, Raquel; Quiroga-García, Raquel; Arenas-Parra, Mar
In this paper, we assess sentiment and emotion regarding green energy through employing a social listening analysis on Twitter. Knowing the sentiment and attitude of the population is important because it will help to promote policies and actions that favor the development of green or renewable energies. We chose to study a crucial period that coincides with the onset of the 2022 Ukrainian–Russo conflict, which has undoubtedly affected global energy policies worldwide. We searched for messages containing the term “green energy” during the days before and after the conflict started. We then performed a semantic analysis of the most frequent words, a comparative analysis of sentiments and emotions in both periods, a dimensionality reduction analysis, and an analysis of the variance of tweets versus retweets. The results of the analysis show that the conflict has changed society’s sentiments about an energy transition to green energy. In addition, we found that negative feelings and emotions emerged in green energy tweeters once the conflict started. However, the emotion of confidence also increased as the conflict, intimately linked to energy, has driven all countries to promote a rapid transition to greener energy sources. Finally, we observed that of the two latent variables identified for social opinion, one of them, pessimism, was maintained while the other, optimism, was subdivided into optimism and expectation.