  Nowadays, geographic information related to Twitter is crucially important
for fine-grained applications. However, the amount of geographic information
avail- able on Twitter is low, which makes the pursuit of many applications
challenging. Under such circumstances, estimating the location of a tweet is an
important goal of the study. Unlike most previous studies that estimate the
pre-defined district as the classification task, this study employs a
probability distribution to represent richer information of the tweet, not only
the location but also its ambiguity. To realize this modeling, we propose the
convolutional mixture density network (CMDN), which uses text data to estimate
the mixture model parameters. Experimentally obtained results reveal that CMDN
achieved the highest prediction performance among the method for predicting the
exact coordinates. It also provides a quantitative representation of the
location ambiguity for each tweet that properly works for extracting the
reliable location estimations.
