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Carrillo de Albornoz Cuadrado, Jorge Amando

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Carrillo de Albornoz Cuadrado
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Jorge Amando
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2017 - 201922020 - 20233
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Mostrando 1 - 5 de 5
  • Miniatura
    Publicación
    Authority and Priority Signals in Automatic Summary Generation for Online Reputation Management
    (Wiley, 2021-05-01) Rodríguez Vidal, Javier; Carrillo de Albornoz Cuadrado, Jorge Amando; Gonzalo Arroyo, Julio Antonio; Plaza Morales, Laura
    Online reputation management (ORM) comprises the collection of techniques that help monitoring and improving the public image of an entity (companies, products, institutions) on the Internet. The ORM experts try to minimize the negative impact of the information about an entity while maximizing the positive material for being more trustworthy to the customers. Due to the huge amount of information that is published on the Internet every day, there is a need to summarize the entire flow of information to obtain only those data that are relevant to the entities. Traditionally the automatic summarization task in the ORM scenario takes some in-domain signals into account such as popularity, polarity for reputation and novelty but exists other feature to be considered, the authority of the people. This authority depends on the ability to convince others and therefore to influence opinions. In this work, we propose the use of authority signals that measures the influence of a user jointly with (a) priority signals related to the ORM domain and (b) information regarding the different topics that influential people is talking about. Our results indicate that the use of authority signals may significantly improve the quality of the summaries that are automatically generated.
  • Miniatura
    Publicación
    EvALL: Open Access Evaluation for Information Access Systems
    (Association for Computing Machinery (ACM), 2017) Almagro Cádiz, Mario; Rodríguez Vidal, Javier; Verdejo, Felisa; Amigo Cabrera, Enrique::virtual::2664::600; Carrillo de Albornoz Cuadrado, Jorge Amando::virtual::2665::600; Gonzalo Arroyo, Julio Antonio::virtual::2666::600; Amigo Cabrera, Enrique; Carrillo de Albornoz Cuadrado, Jorge Amando; Gonzalo Arroyo, Julio Antonio; Amigo Cabrera, Enrique; Carrillo de Albornoz Cuadrado, Jorge Amando; Gonzalo Arroyo, Julio Antonio; Amigo Cabrera, Enrique; Carrillo de Albornoz Cuadrado, Jorge Amando; Gonzalo Arroyo, Julio Antonio
    The EvALL online evaluation service aims to provide a unified evaluation framework for Information Access systems that makes results completely comparable and publicly available for the whole research community. For researchers working on a given test collection, the framework allows to: (i) evaluate results in a way compliant with measurement theory and with state-of-the-art evaluation practices in the field; (ii) quantitatively and qualitatively compare their results with the state of the art; (iii) provide their results as reusable data to the scientific community; (iv) automatically generate evaluation figures and (low-level) interpretation of the results, both as a pdf report and as a latex source. For researchers running a challenge (a comparative evaluation campaign on shared data), the framework helps them to manage, store and evaluate submissions, and to preserve ground truth and system output data for future use by the research community. EvALL can be tested at http://evall.uned.es.
  • Miniatura
    Publicación
    Automatic Generation of Entity-Oriented Summaries for Reputation Management
    (Springer, 2020-04-01) Rodríguez Vidal, Javier; Verdejo, Julia; Carrillo de Albornoz Cuadrado, Jorge Amando; Amigo Cabrera, Enrique; Plaza Morales, Laura; Gonzalo Arroyo, Julio Antonio
    Producing online reputation summaries for an entity (company, brand, etc.) is a focused summarization task with a distinctive feature: issues that may affect the reputation of the entity take priority in the summary. In this paper we (i) present a new test collection of manually created (abstractive and extractive) reputation reports which summarize tweet streams for 31 companies in the banking and automobile domains; (ii) propose a novel methodology to evaluate summaries in the context of online reputation monitoring, which profits from an analogy between reputation reports and the problem of diversity in search; and (iii) provide empirical evidence that producing reputation reports is different from a standard summarization problem, and incorporating priority signals is essential to address the task effectively.
  • Miniatura
    Publicación
    Feature engineering for sentiment analysis in e-health forums
    (Public Library of Science, 2018-11-29) Rodríguez Vidal, Javier; Carrillo de Albornoz Cuadrado, Jorge Amando; Plaza Morales, Laura; Teomiro García, Ismael Iván
    Introduction Exploiting information in health-related social media services is of great interest for patients, researchers and medical companies. The challenge is, however, to provide easy, quick and relevant access to the vast amount of information that is available. One step towards facilitating information access to online health data is opinion mining. Even though the classification of patient opinions into positive and negative has been previously tackled, most works make use of machine learning methods and bags of words. Our first contribution is an extensive evaluation of different features, including lexical, syntactic, semantic, network-based, sentiment-based and word embeddings features to represent patient-authored texts for polarity classification. The second contribution of this work is the study of polar facts (i.e. objective information with polar connotations). Traditionally, the presence of polar facts has been neglected and research in polarity classification has been bounded to opinionated texts. We demonstrate the existence and importance of polar facts for the polarity classification of health information. Material and methods We annotate a set of more than 3500 posts to online health forums of breast cancer, crohn and different allergies, respectively. Each sentence in a post is manually labeled as “experience”, “fact” or “opinion”, and as “positive”, “negative” and “neutral”. Using this data, we train different machine learning algorithms and compare traditional bags of words representations with word embeddings in combination with lexical, syntactic, semantic, network-based and emotional properties of texts to automatically classify patient-authored contents into positive, negative and neutral. Beside, we experiment with a combination of textual and semantic representations by generating concept embeddings using the UMLS Metathesaurus. Results We reach two main results: first, we find that it is possible to predict polarity of patient-authored contents with a very high accuracy (≈ 70 percent) using word embeddings, and that this considerably outperforms more traditional representations like bags of words; and second, when dealing with medical information, negative and positive facts (i.e. objective information) are nearly as frequent as negative and positive opinions and experiences (i.e. subjective information), and their importance for polarity classification is crucial.
  • Miniatura
    Publicación
    A systematic review on media bias detection: What is media bias, how it is expressed, and how to detect it
    (Elsevier, 2023-09-26) Rodrigo Ginés, Francisco Javier; Carrillo de Albornoz Cuadrado, Jorge Amando; Plaza Morales, Laura; https://orcid.org/0000-0001-6235-6860
    Media bias and the intolerance of media outlets and citizens to deal with opposing points of view pose a threat to the proper functioning of democratic processes. In this respect, we present a systematic review of the literature related to media bias detection, in order to characterize and classify the different types of media bias, and to explore the state-of-the-art of automatic media bias detection systems. The main objectives of this paper were twofold. First, we framed information, misinformation and disinformation within a theoretical framework that allows us to differentiate the different existing misinformation problems such as us media bias, fake news, or propaganda. Second, we studied the state of the art of automatic media bias detection systems: analyzing the most recently used techniques and their results, listing the available resources and the most relevant datasets, and establishing a discussion about how to increase the maturity of this area. After doing a comprehensive literature review, we have identified and selected a total of 17 forms of media bias that can be classified depending on the context (e.g., coverage bias, gatekeeping bias, or statement bias), and on the author’s intention (e.g., spin bias, or ideology bias). We also reviewed, following the PRISMA methodology, the main automatic media bias detection systems that have been developed so far, selecting 63 relevant articles, from which we extracted the most used techniques; including non-deep learning methods (e.g., linguistic-based methods, and reported speech-based methods), and deep learning methods (e.g., RNNs-based methods, and transformers-based methods). Additionally, we listed and summarized 18 available datasets for the task of automatic media bias detection. In conclusion, the current methods for automatic media bias detection are still in their infancy and there is still a lot of potential for improvement in terms of accuracy and robustness. We have proposed some future research lines that could potentially contribute to the development of more advanced techniques.