Tom De Smedt

Media coverage in times of political crisis: A text mining approach

At the year end of 2011 Belgium formed a government, after a world record breaking period of 541days of negotiations. We have gathered and analysed 68,000 related on-line news articles published in 2011 in Flemish newspapers. These articles were analysed by a custom-built expert system. The results of our text mining analyses show interesting differences in media coverage and votes for several political parties and politicians. With opinion mining, we are able to automatically detect the sentiment of each article, thereby allowing to visualise how the tone of reporting evolved throughout the year, on a party, politician and newspaper level. Our suggested framework introduces a generic text mining approach to analyse media coverage on political issues, including a set of methodological guidelines, evaluation metrics, as well as open source opinion mining tools. Since all analyses are based on automated text mining algorithms, an objective overview of the manner of reporting is provided. The analysis shows peaks of positive and negative sentiments during key moments in the negotiation process.

Evaluating and understanding text-based stock price prediction models

Despite the fact that both the Efficient Market Hypothesis and Random Walk Theory postulate that it is impossible to predict future stock prices based on currently available information, recent advances in empirical research have been proving the opposite by achieving what seems to be better than random prediction performance. We discuss some of the (dis)advantages of the most widely used performance metrics and conclude that is difficult to assess the external validity of performance using some of these measures. Moreover, there remain many questions as to the real-world applicability of these empirical models. In the first part of this study we design novel stock price prediction models, based on state-of-the-art text-mining techniques to assert whether we can predict the movement of stock prices more accurately by including indicators of irrationality. Along with this, we discuss which metrics are most appropriate for which scenarios in order to evaluate the models. Finally, we discuss how to gain insight into text-mining-based stock price prediction models in order to evaluate, validate and refine the models.

VALENCE: affective visualisation using EEG

VALENCE is an interactive visualisation controlled by live brainwave monitoring. We used a wireless EEG headset to monitor the players alpha waves (an indicator of relaxation) and valence (an indicator of emotion or arousal). The game world is an emergent system of attractive and repulsive forces responding to EEG input.

Using Wiktionary to Build an Italian Part-of-Speech Tagger

While there has been a lot of progress in Natural Language Processing (NLP), many basic resources are still missing for many languages, including Italian, especially resources that are free for both research and commercial use. One of these basic resources is a Part-of-Speech tagger, a first processing step in many NLP applications. We describe a weakly-supervised, fast, free and reasonably accurate part-of-speech tagger for the Italian language, created by mining words and their part-of-speech tags from Wiktionary. We have integrated the tagger in Pattern, a freely available Python toolkit. We believe that our approach is general enough to be applied to other languages as well.

Generative art inspired by nature, using NodeBox

NodeBox is a free application for producing generative art. This paper gives an overview of the nature-inspired functionality in NodeBox and the artworks we created using it. We demonstrate how it can be used for evolutionary computation in the context of computer games and art, and discuss some of our recent research with the aim to simulate (artistic) brainstorming using language processing techniques and semantic networks.

Medical Art Therapy of the Future: Building an Interactive Virtual Underwater World in a Children’s Hospital

We are developing an interactive virtual underwater world with the aim to reduce stress and boredom in hospitalised children, to improve their quality of life, by employing an evidence-based design process and by using techniques from Artificial Life and Human-Computer Interaction. A 3D motion sensing camera tracks the activity of children in front of a wall projection. As they wave their hands, colorful sea creatures paddle closer to say hi and interact with the children.