A Big Linked Data Toolkit for Social Media Analysis and Visualization based on W3C Web Components

J. Fernando Sánchez-Rada, Alberto Pascual-Saavedra, Enrique Conde-Sánchez & Carlos A. Iglesias (2018). A Big Linked Data Toolkit for Social Media Analysis and Visualization based on W3C Web Components. In Hervé Panetto, Christophe Debruyne, Henderik A. Proper, Claudio A. Ardagna, Dumitru Roman & Robert Meersman (editors), On the Move to Meaningful Internet Systems. OTM 2018 Conferences. Part II, pages 498-515. Valletta, Malta : Springer-Verlag.

Social media generates a massive amount of data at a veryfast pace. Objective information such as news, and subjective contentsuch as opinions and emotions are intertwined and readily available. Thisdata is very appealing from both a research and a commercial point ofview, for applications such as public polling or marketing purposes. Acomplete understanding requires a combined view of information fromdifferent sources which are usually enriched (e.g .sentiment analysis) andvisualized in a dashboard.In this work, we present a toolkit that tackles these issues on differentlevels: 1) to extract heterogeneous information, it provides independentdata extractors and web scrapers; 2) data processing is done with in-dependent semantic analysis services that are easily deployed; 3) a con-figurable Big Data orchestrator controls the execution of extraction andprocessing tasks; 4) the end result is presented in a sensible and inter-active format with a modular visualization framework based on WebComponents that connects to different sources such as SPARQL andElasticSearch endpoints. Data workflows can be defined by connectingdifferent extractors and analysis services. The different elements of thistoolkit interoperate through a linked data principled approach and a setof common ontologies. To illustrate the usefulness of this toolkit, thiswork describes several use cases in which the toolkit has been success-fully applied.