With the explosive popularity of various social network services (SNSs), an enormous number of user documents are generated and shared daily by users. Considering the volume of user documents, efficient methods for grouping or searching relevant user documents are required. In the case of Twitter, self-defined metadata called hashtags are attached to tweets for that purpose. However, due to the wide scope of hashtags, users are having difficulty in finding out appropriate hashtags for their tweets. In this paper, we propose a new hashtag recommendation scheme for user tweets based on user tweet analysis and hashtag classification. More specifically, we extract keywords from user tweets using TF-IDF and classify their hashtags into pre-defined classes using Naïve Bayes classifier. Next, we select a user interest class based on keywords of user tweets to reflect user interest. To recommend appropriate hashtags to users, we calculate the ranks of candidate hashtags by considering similar tweets, user interest and popularity of hashtags. To show the performance of our scheme, we developed an Android application named “TWITH” and evaluate its recommendation accuracy. Through various experiments, we show that our scheme is quite effective in the hashtag recommendation.