Techniques for the automated analysis of large text contributions and the identification of topics, argument structures, sentiments and emotions.
- How can text contents be automatically clustered and classified?
- How can expressive sentiments be distinguished from opinions and arguments?
- How can the design of input formats support automated analysis?
- How can patient discussions on diabetic online forums be automatically summarized and interpreted?
Prof. Dr. Stefan Conrad (vice speaker)
Board, Computer Science, DIID-Team
Stefan Conrad is full professor in computer science at Heinrich Heine University in Duesseldorf since 2002. He has a chair for databases and information systems. Since 2015 he is member of the Academic Senate of the Heinrich Heine University.
His research considers the analysis of large data sets, in particular, he is interested in image retrieval, the analysis of large time series, clustering, and text mining. He has on-going cooperations with industrial partners. Several of these cooperations were funded by the BMWi (Federal Ministry for Economic Affairs and Energy) in a research and development programme for small and medium enterprises. These projects dealt with opinion mining (sentiment analysis), extraction of product features relevant for users, and automated text summarization. At DIID his research interest is currently focused on automated topic extraction and content analyses of texts as well as identifying argument structures, sentiments, and emotions.