The future-related information mining task for online web resources such as news articles and blogs has been getting more attention due to its potential usefulness in supporting individual's decision making in a world where massive new data are generated daily. Instead of building a data-driven model to predict the future, one extracts future events from these massive data with high probability that they occur at a future time and a specific geographic location. Such spatiotemporal future

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... can be utilized by a recommender system on a location-aware device to provide localized future event suggestions. In this paper, we describe a systematic approach for mining future spatiotemporal events from web; in particular, news articles. In our application context, a valid event is defined both spatially and temporally. The mining procedure consists of two main steps: recognition and matching. For the recognition step, we identify and resolve toponyms (geographic location) and future temporal patterns. In the matching step, we perform spatiotemporal disambiguation, de-duplication, and pairing. To provide more useful future event guidance, we attach to each event a sentiment linguistic variable: positive, negative, or neutral, so that one may use these extracted event information for recommendation purposes in the form of "avoid Event A" or "avoid geographic location L at time T" or "attend Event B" based on the event sentiment. The identified future event consists of its geographic location, temporal pat-