Social media services like Twitter churn out user-generated content in vast amounts. The massive availability of this kind of data demands new forms of analysis and visualization, to make it accessible and interpretable. In this article, we introduce Twista, an application that can be used to create tailored tweet collections according to specific filter criteria, such as the occurrence of certain keywords or hashtags. Once the tweet collection has been created, Twista calculates basic statistics, e.g. the average tweet length or the most active user. Furthermore, the application can perform basic sentiment analysis, analyze tweets with regard to their date of publication, and analyze the communication between different Twitter users. The results of these analyses are visualized by means of the data driven documents toolkit (d3.js) and can be viewed directly in the browser, or are available for download in PDF and JSON format. We also present three exemplary use cases that illustrate the possible use of Twista for different scenarios.