We study the problem of predicting likely places of visit of users using their past tweets. What people write on their microblogs reflects their intent and desire relating to most of their common day interests. Taking this as a strong evidence, we hypothesize that tweets of the person can also be treated as source of strong indicator signals for predicting their places of visits. In this paper, we propose a novel approach for predicting place of visit within a given geospatial range considering the past tweets and the time of visit. These predictions can be used for generating places recommendation or for promotions. In this approach, we analyze use of various features that can be extracted from the historical tweets—for example, personality traits estimated from the past tweets and the actual words mentioned in the tweets. We performed extensive empirical experiments involving, real data derived from twitter timelines of 4600 persons with multi-label classification as predictive model. The performances of proposed approach outperform the four baselines with accuracy reaching 90 % for top five predictions. Based on our experimental study, we come up with general guidelines on building the prediction model in terms of the type of features extracted from historical tweets, window size of historical tweets and on the optimal radius of query around the place of visit at a given time.