Project - Financial Twitter Tracker

The Intelligent Systems Group (GSI, Grupo de Sistemas Inteligentes) is a research group acknowledged by the Technical University of Madrid (UPM, Universidad Politécnica de Madrid). Our staff consists of 14 professors, 8 researchers and 8 undergraduates from several departments of the university, mainly from the Department of Telematic Systems Engineering (DIT, Departamento de Ingeniería de Sistemas Telemáticos).

Since 1982, our group has conducted research, development and innovation in theories, methods and applications of artificial intelligence techniques, especially in the fields of Machine learning and Big Data, Semantic technologies and Linked Data, Intelligent Agents and Agent-based Social Simulation, Language engineering, and Web and Service engineering.

Our group proposes master thesis and final graduate works in topics such as Machine Learning, Big Data and the Web of Data. We offer the possibility of publishing the results in international conferences and workshops.

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GSI has developed a multiagent system for telecommunication network diagnosis that is currently being used by Telefónica in Spain and O2 in Czech Republic

GSI participates in the project MOSI-AGIL which researches on the application of social simulation techniques for evacuation of big facilities. The project is funded by the Region of Madrid.

The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation. - Tim Berners-Lee

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Published Paper: "Contextualization of a Radical Language Detection System Through Moral Values and Emotions"

The article Contextualization of a Radical Language Detection System Through Moral Values and Emotions has been recently published in the IEEE Access journal (JCR Q2 2022, 3.9 IF). The publicacion is authored by Pat ...

Final Conference Project PARTICIPATION

GSI is participating in the final conference of the project PARTICIPATION in Rome. The conference showcases the innovative and participatory methods and tools that the project has developed and tested for analysing ...

Published paper "Detection of the Severity Level of Depression Signs in Text Combining a Feature-Based Framework with Distributional Representations" in Applied Sciences

The article "Detection of the Severity Level of Depression Signs in Text Combining a Feature-Based Framework with Distributional Representations ", by Sergio Muñoz and Carlos A. Iglesias has been published in the A ...

GSI Channel

Financial Twitter Tracker

Status

Finished

Proposed start date

2011-09-01

Proposed end date

2013-12-31

Members

Carlos A. Iglesias; Mercedes Garijo; J. Fernando Sánchez-Rada; Javier Espinosa; Toni Prada; Marcos Torres

Description

Proyecto
El objetivo principal de Financial Twitter Tracker es el enriquecimiento de contenidos financieros con información extraía de medios sociales de Twitter, así como la detección de demanda de nuevos contenidos financieros en determinados tópicos. Con este fin, se realizará un sistema de análisis y evaluación de información financiera en Twitter que extraiga, monitorice, visualice y evalúe el peso, variación, propagación temporal y las relaciones de los datos.

Los objetivos específicos del proyecto Financial Twitter Tracker son:

Enriquecimiento de contenidos basados en tópicos descubiertos en Twitter.
Mejora de la cadena de valor de la creación y distribución de contenidos, conectando la demanda detectada con la generación de contenidos, catalogación y distribución de contenidos.
Crear un sistema de rastreo y adquisición de datos en Twitter basado en tecnologías lingüísticas y semánticas que sea capaz de extraer la información relevante de forma inteligente.
Analizar la propagación de información en Twitter tratando de inferir patrones de comportamiento.
Profundizar en el uso de técnicas avanzadas de visualización que permitan representar de forma gráfica la propagación de memes de Twitter y sus relaciones.
Crear un sistema de almacenamiento para grandes volúmenes de datos utilizando bases de datos no relacionales, capaz de albergar datos complejos con un rendimiento adecuado.

Consorcio:
Paradigma Tecnológico, FinancialRed Networks, Innoland Gestión de la Innovación y Universidad Politécnica de Madrid

Proyecto cofinanciado por:
Ministerio de Industria, Energía y Turismo dentro del Plan Nacional de Investigación Científica, Desarrollo e Innovación Tecnológica 2008-2011 – TSI-090100-2011-114