Text analysis of user-generated contents for health-care applications: case study on smoking status classification

Text mining techniques have demonstrated a potential to unlock significant patient health information from unstructured text. However, most of the published work has been done using clinical reports, which are difficult to access due to patient confidentiality. In this paper, we present an investigation of text analysis for smoking status classification from User-Generated Contents (UGC), such as online forum discussions. UGC are more widely available, compared to clinical reports. Based on analyzing the properties of UGC, we propose the use of Linguistic Inquiry Word Count (LIWC) an approach being used for the first time for such a health-related task. We also explore various factors that affect the classification performance. The experimental results and evaluation indicate that the forum classification performs well with the proposed features. It has achieved an accuracy of up to 75% for smoking status prediction. Furthermore, the utilized features set is compact (88 features only) and independent of the dataset size

El rayo de la moda en el espejo del "eWOM" y los estereotipos de consumidores = The fashion ray in the mirror of the eWOM and consumer stereotypes

El uso de Internet ha transformado la publicidad tradicional, especialmente el boca a boca, que se ha trasladado a los canales digitales como medio principal. Esta transformación ha supuesto que el marketing de recomendación se convierta en un elemento clave para cualquier empresa. A raíz de esto han surgido nuevos conceptos como el electronic word of mouth o los microinfluencers, que representan herramientas de comunicación de gran utilidad para los profesionales del marketing. Pero para ello, se deben conocer las tipologías de consumidores y los canales en los que estos se mueven. El presente trabajo pretende analizar en profundidad el concepto eWOM, estudiando cuáles son las motivaciones que llevan a los consumidores a buscar o generar este tipo de información. Además, pretende también establecer las principales características que definen las tipologías de consumidores. Todo ello englobado en el sector de la moda en España

Social Data Mining for Crime Intelligence

With the advancement of the Internet and related technologies, many traditional crimes have made the leap to digital environments. The successes of data mining in a wide variety of disciplines have given birth to crime analysis. Traditional crime analysis is mainly focused on understanding crime patterns, however, it is unsuitable for identifying and monitoring emerging crimes. The true nature of crime remains buried in unstructured content that represents the hidden story behind the data. User feedback leaves valuable traces that can be utilised to measure the quality of various aspects of products or services and can also be used to detect, infer, or predict crimes. Like any application of data mining, the data must be of a high quality standard in order to avoid erroneous conclusions. This thesis presents a methodology and practical experiments towards discovering whether (i) user feedback can be harnessed and processed for crime intelligence, (ii) criminal associations, structures, and roles can be inferred among entities involved in a crime, and (iii) methods and standards can be developed for measuring, predicting, and comparing the quality level of social data instances and samples. It contributes to the theory, design and development of a novel framework for crime intelligence and algorithm for the estimation of social data quality by innovatively adapting the methods of monitoring water contaminants. Several experiments were conducted and the results obtained revealed the significance of this study in mining social data for crime intelligence and in developing social data quality filters and decision support systems