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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 - 20182
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Fecha de finalización: 2019 × Fecha de inicio: 2017 ×

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Mostrando 1 - 2 de 2
  • 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
    EvALL: Open Access Evaluation for Information Access Systems
    (Association for Computing Machinery (ACM), 2017) Almagro Cádiz, Mario; Rodríguez Vidal, Javier; Verdejo, M. Felisa; 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.