How can we estimate the location of user-generated content using textual data without location-specific information to understand urban space? Understanding urban space is important to tackle the issues that cities face, e.g. disasters, traffic congestion. Although event information reported with location data on microblog are very informational, many users post them without their locations because of the privacy concerns. To address this issue, some studies have attempted to estimate the location where the users post their tweets by analyzing the text. While those works have introduced various techniques for effective estimation, they have taken a lot of effort to do so. In this paper, we propose an approach that can estimate the location without those efforts. To achieve this goal, we adopt bidirectional Long-Short Term Memory (BLSTM). In our experiment, we use the geotagged tweets that were posted in Japan and treat location estimation as a multi-class classification problem where the location of tweets should be classified into administrative districts. As a result, we show that our proposed method can classify the location of tweets with higher accuracy than baseline methods.