Knowledge about current user preferences and needs regarding electric vehicles is key for developing convenient solutions in the field of e-mobility. They are decisive for a successful market uptake of electric vehicles. In this article, we analyze German Twitter data as fast and unbiased feedback from customers on current products. We demonstrate how this new data source can be applied in the field of e-mobility. We compare our analysis to the traditional approach of a literature survey. Results show that for the German-speaking region the frequency distribution of needs derived from the literature survey differs significantly from the frequency distribution derived from the sample of tweets. Price-related needs and needs concerning Car characteristics are comparably overrepresented in the literature survey. On the other hand, Charging-related needs in particular as well as needs classified in the society cluster are overrepresented in the Twitter data set. We derive interpretations from these insights and illustrate possibilities for stakeholders and policymakers to increase acceptance.