This paper proposes and evaluates a method to detect and classify tweets that are triggered by places where users locate. Recently, many related works address to detect real world events from social media such as Twitter. However, geotagged tweets often contain noise, which means tweets which are not content-wise related to users' location. This noise is problem for detecting real world events. To address and solve the problem, we define the Place-Triggered Geotagged Tweet, meaning tweets which have both geotag and content-based relation to users' location. We designed and implemented a keyword-based matching technique to detect and classify place-triggered geotagged tweets. We evaluated the performance of our method against a ground truth provided by 18 human classifiers, and achieved 82% accuracy. Additionally, we also present two example applications for visualizing place-triggered geotagged tweets.