A Fine Grain Sentiment Analysis with Semantics in Tweets

Social networking is nowadays a major source of new information in the world. Microblogging sites like Twitter have millions of active users (320 million active users on Twitter on the 30th September 2015) who share their opinions in real time, generating huge amounts of data. These data are, in most cases, available to any network user. The opinions of Twitter users have become something that companies and other organisations study to see whether or not their users like the products or services they offer. One way to assess opinions on Twitter is classifying the sentiment of the tweets as positive or negative. However, this process is usually done at a coarse grain level and the tweets are classified as positive or negative. However, tweets can be partially positive and negative at the same time, referring to different entities. As a result, general approaches usually classify these tweets as “neutral”. In this paper, we propose a semantic analysis of tweets, using Natural Language Processing to classify the sentiment with regards to the entities mentioned in each tweet. We offer a combination of Big Data tools (under the Apache Hadoop framework) and sentiment analysis using RDF graphs supporting the study of the tweet’s lexicon. This work has been empirically validated using a sporting event, the 2014 Phillips 66 Big 12 Men’s Basketball Championship. The experimental results show a clear correlation between the predicted sentiments with specific events during the championship

TITAN: A knowledge-based platform for Big Data workflow management

Modern applications of Big Data are transcending from being scalable solutions of data processing and analysis, to now provide advanced functionalities with the ability to exploit and understand the underpinning knowledge. This change is promoting the development of tools in the intersection of data processing, data analysis, knowledge extraction and management. In this paper, we propose TITAN, a software platform for managing all the life cycle of science workflows from deployment to execution in the context of Big Data applications. This platform is characterised by a design and operation mode driven by semantics at different levels: data sources, problem domain and workflow components. The proposed platform is developed upon an ontological framework of meta-data consistently managing processes and models and taking advantage of domain knowledge. TITAN comprises a well-grounded stack of Big Data technologies including Apache Kafka for inter-component communication, Apache Avro for data serialisation and Apache Spark for data analytics. A series of use cases are conducted for validation, which comprises workflow composition and semantic meta-data management in academic and real-world fields of human activity recognition and land use monitoring from satellite images.This work has been partially funded by the Spanish Ministry of Science and Innovation via Grant PID2020 112540RB-C41 (AEI/FEDER, UE) and Andalusian PAIDI program with grant P18-RT-2799. Funding for open access charge: Universidad de Málaga / CBUA

Design and rationale of a multicentre, randomised, double-blind, placebo-controlled clinical trial to evaluate the effect of vitamin D on ventricular remodelling in patients with anterior myocardial infarction: the VITamin D in Acute Myocardial Infarction (VITDAMI) trial

Introduction:Decreased plasma vitamin D (VD) levels are linked to cardiovascular damage. However, clinical trials have not demonstrated a benefit of VD supplements on left ventricular (LV) remodelling. Anterior ST-elevation acute myocardial infarction (STEMI) is the best human model to study the effect of treatments on LV remodelling. We present a proof-of-concept study that aims to investigate whether VD improves LV remodelling in patients with anterior STEMI. Methods and analysis:The VITamin D in Acute Myocardial Infarction (VITDAMI) trial is a multicentre, randomised, double-blind, placebo-controlled trial. 144 patients with anterior STEMI will be assigned to receive calcifediol 0.266 mg capsules (Hidroferol SGC)/15 days or placebo on a 2:1 basis during 12 months. Primary objective:to evaluate the effect of calcifediol on LV remodelling defined as an increase in LV end-diastolic volume >= 10\% (MRI). Secondary objectives:change in LV end-diastolic and end-systolic volumes, ejection fraction, LV mass, diastolic function, sphericity index and size of fibrotic area; endothelial function; plasma levels of aminoterminal fragment of B-type natriuretic peptide, galectin-3 and monocyte chemoattractant protein-1; levels of calcidiol (VD metabolite) and other components of mineral metabolism (fibroblast growth factor-23 (FGF-23), the soluble form of its receptor klotho, parathormone and phosphate). Differences in the effect of VD will be investigated according to the plasma levels of FGF-23 and klotho. Treatment safety and tolerability will be assessed. This is the first study to evaluate the effect of VD on cardiac remodelling in patients with STEMI. Ethics and dissemination: This trial has been approved by the corresponding Institutional Review Board (IRB) and National Competent Authority (Agencia Espanola de Medicamentos y Productos Sanitarios (AEMPS)). It will be conducted in accordance with good clinical practice (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use-Good Clinical Practice (ICH-GCP)) requirements, ethical principles of the Declaration of Helsinki and national laws. The results will be submitted to indexed medical journals and national and international meetings.The VITDAMI trial is an investigator initiated study, sponsored by the Instituto de Investigacion Sanitaria Fundacion Jimenez Diaz (IIS-FJD). Funding has been obtained from Fondo de Investigaciones Sanitarias (PI14/01567; http://www.isciii.es/) and Spanish Society of Cardiology (http://secardiologia.es/). In addition, the study medication has been provided freely by the pharmaceutical Company FAES FARMA S.A. (Leioa, Vizcaya, Spain; http://faesfarma.com/). This company was the only funder who collaborated in study design (IG-H).S