Mining future spatiotemporal events and their sentiment from online news articles for location-aware recommendation system

Abstract

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 mak-ing 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 events can be utilized by a recommender sys-tem on a location-aware device to provide localized future event suggestions. In this paper, we describe a systematic approach for mining fu-ture 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