PoliTwi: Early Detection of Emerging Political Topics on Twitter and the Impact on Concept-Level Sentiment Analysis
Sven Rill, Dirk Reinela, Jörg Scheidt, Institute of Information Systems, University of Applied Sciences Hof, Alfons-Goppel-Platz 1, Hof, Germany
Roberto V. Zicari, Institute of Computer Science, Big Data Lab, Goethe University Frankfurt, Robert-Mayer-Str. 10, Frankfurt am Main, Germany
In this work, we present a system called PoliTwi, which was designed to detect emerging political topics (Top Topics) in Twitter sooner than other standard information channels. The recognized Top Topics are shared via different channels with the wider public. For the analysis, we have collected about 4,000,000 tweets before and during the parliamentary election 2013 in Germany, from April until September 2013. It is shown, that new topics appearing in Twitter can be detected right after their occurrence. Moreover, we have compared our results to Google Trends. We observed that the topics emerged earlier in Twitter than in Google Trends.
Finally, we show how these topics can be used to extend existing knowledge bases (web ontologies or semantic networks) which are required for concept- level sentiment analysis. For this, we utilized special Twitter hashtags, called sentiment hashtags, used by the German community during the parliamen- tary election.
Keywords: Topic Detection, Concept-Level Sentiment Analysis, Big Data, Twitter, Social Data Analysis
– LINK to PoliTwi service online: PoliTwi
– PoliTwi on Twitter: @politwi
PoliTwi: Early Detection of Emerging Political Topics on Twitter and the Impact on Concept-Level Sentiment Analysis,
S. Rill, D. Reinel, J. Scheidt, R.V. Zicari,
Knowledge-Based Systems (2014), doi: http://dx.doi.org/10.1016/j.knosys.2014.05.008