Demos

Sefarad 3.0

Sefarad 3.0
  • Authors Enrique Conde
  • Description Sefarad 3.0 is a visualization tool for Big Data and semantic data sources, made with ElasticSearch and W3C web components.

Football-Mood

Football-Mood
  • Authors Alberto Pascual Saaevedra
  • Description This demo shows an application of Sefarad (Visualisation Framework) and Senpy ( Sentiment and Emotion Analysis) through the analysis of Tweets of a football match.

Financial Twitter Tracker

Financial Twitter Tracker
  • Authors J. Fernando Sánchez-Rada, Marcos Torres, Miguel Coronado, Álvaro Carrera
  • Description Tweets sentiment visualization tool on different listed companies with the aim of studying the correlation between this sentiment and the stock market movement. Demo: Demo

Sefarad

Sefarad
  • Authors Roberto Bermejo, Rubén Díaz Vega, Marcos Torres, Miguel Coronado, Álvaro Carrera
  • Description Sefarad is an HTML5 Framework developed to analyze data by making SPARQL queries to the backend of your choice. In this demo, queries are made to DBpedia about European universities, with some widgets that allow you to filter the results and display them on a map.

Job Matching

Job Matching

SmartOpenData visualization tool

SmartOpenData visualization tool

Eurosentiment Marl Generator

Eurosentiment Marl Generator

Emotion Detection with Onyx

Emotion Detection with Onyx
  • Authors J. Fernando Sánchez-Rada, Marcos Torres
  • Description Tool that facilitates the analysis of emotions and their modeling with the onyx vocabulary.

Bot GSI

Bot GSI
  • Authors Javier Herrera
  • Description Bot that combines technology from Bots (ChatScript), Agents (Jason), semantic indexing (Scrappy and Siren).

Wool

Wool
  • Authors Óscar Araque Iborra
  • Description Tweets sentiment visualization tool on different listed companies with the aim of studying the correlation between this sentiment and the stock market movement.

SEAS

SEAS
  • Authors David Moreno Briz
  • Description SEAS is a set of services for the analysis of feelings and emotions according to the NIF API. The NLP exchange format (NIF) is a format based on RDF and OWL. The analysis of feelings is generated using the Marl semantic technology; While the analysis of emotions uses Onyx.