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Automatische Erkennung von politischen Trends mit Twitter – brauchen wir Meinungsumfragen noch?
(2017)
Meinungsforschungsinstitute betreiben einen beträchtlichen Aufwand, um die Meinungstrends der Bevölkerung bezogen auf Politiker mit Telefon- und Straßenumfragen zu erfassen. Mit einer Studierendengruppe haben wir uns im Winter 2015/16 die Frage gestellt, ob es möglich ist, diesen Prozess zu automatisieren. Die Idee dahinter ist, dass die Plattform Twitter vielfach für politische Diskussionen genutzt wird. Da sich Tweets auf einen Umfang von 140 Zeichen beschränken und das jeweilige Thema durch Hashtags meist eindeutig zugeordnet werden kann, scheinen sich Twitter-Daten gut für eine automatische Sentiment-Analyse zu eignen. Mit Sentiment-Analyse-Methoden kann man diese Tweets automatisch in positive und negative Meinungsäußerungen klassifizieren. Wir haben dafür einen Twitter-Crawler und Sentiment-Analyse in der Programmiersprache Python implementiert. Anschließend haben wir über einen Zeitraum von vier Wochen Tweets zu Politikern gesammelt und die Ergebnisse der Meinungsanalysen visualisiert. Schließlich haben wir unsere Ergebnisse mit dem ZDF-Politbarometer verglichen.
The relevance of Machine Intelligence, a.k.a. Artificial Intelligence (AI), is undisputed at the present time. This is not only due to AI successes in research but, more prominently, its use in day-to-day practice. In 2014, we started a series of annual workshops at the Leibniz Zentrum für Informatik, Schloss Dagstuhl, Germany, initially focussing on Corporate Semantic Web and later widening the scope to Applied Machine Intelligence. This article presents a number of AI applications from various application domains, including medicine, industrial manufacturing and the insurance sector. Best practices, current trends, possibilities and limitations of new AI approaches for developing AI applications are also presented. Focus is put on the areas of natural language processing, ontologies and machine learning. The article concludes with a summary and outlook.
The benefits of ideation for both industry and academia alike have been outlined by countless studies, leading to research into various approaches attempting to add new ideation methods or examine how the quality of the ideas and solutions created can be measured. Although AI-based approaches are being researched, there is no attempt to provide the ideation participants with information that inspire new ideas and solutions in real time. Our proposal presents a novel and intuitive approach that supports users in real time by providing them with relevant information as they conduct ideation. By analyzing their ideas within the respective ideation sessions, our approach recommends items of interest with high contextual similarity to the proposed ideas, allowing users to skim through, for example, publications and inspire new ideas quickly. The recommendations also evolve in real time. As more ideas are written during the ideation session, the recommendations become more precise. This real-time approach is instantiated with various ideation methods as a proof of concept, and various models are evaluated and compared to identify the best model for working with ideas.