Research on sentiment analysis has a wide variety of economical and social applications, from finding out which books your friends will like or predicting the stock market to aiding in detecting depressed teenagers on social networking sites. During the course of 2012, we have applied sentiment analysis to various text sources with political content. This has resulted in one journal publication and a good reception by the Belgian media.

Media coverage in times of crisis

Abstract: At the year end of 2011 Belgium formed a government, after a world record breaking period of 541 days 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 dierences 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 visualize 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.
Publication: available here

Political barometer

Enric Junqué de Fortuny (Applied Data Mining) and Tom De Smedt (CLiPS) were interviewed for television by ATV. They discuss the, an online sentiment analysis tool for political discourse, used to monitor Twitter news about the upcoming Belgian 2012 local elections.

Source: ATV