Visual-Interactive Text Analysis to Support Political Decision Making - From Sentiments to Arguments to Policies

Abstract

Political decision making involves the evaluation of alternative solutions (so called policy models) to a given societal problem and the selection of the most promising one. Large amounts of textual information to be considered in decision making processes can be found on the web. This includes general information about policy models, individual arguments in favor or against these policies, and public opinions. Monitoring large text collections and extracting the relevant information is time consuming. In this approach we present a visual analytics system that supports users in assessing the results of automatic text analysis methods. The methods extract text segments from large document collections and associate them with predefined policy domains, policy models, and policy arguments. Moreover, sentiment analysis is applied on the text segments. Visualization techniques provide non-IT experts an intuitive access to the results. With the system, users can monitor public debates on the web. In addition, we analyze concepts that enable the user to give visual-interactive feedback on the text analysis results. This direct user feedback can help to improve the accuracy of individual text analysis modules and the credibility of the overall text analysis process. The system was tested with real users from the political decision making domain.