How fashionable is each street?: quantifying road characteristics using social media
Takuya Nishimura, Kyosuke Nishida, Hiroyuki Toda, Hiroshi Sawada · Advances in Social Networks Analysis and Mining · 2016
Determining routes that provide opportunities to satisfy the various demands of users is still an open problem. This is because it is virtually impossible to manually quantify the characteristics of each road and there are few resources describing roads directly such that we meet any demand that may arise. The goal of this study is to automatically quantify the characteristics of roads for demands that can be described using keywords such as fashionable. To achieve this goal, we propose a two-stage method that analyzes social media and road networks. First, our method estimates the topic distribution (i.e., the characteristics) of each point-of-interest (POI) by analyzing geo-tagged texts with the Latent Dirichlet Allocation model. Next, it uses a Markov random field model to estimate the characteristics of each road on the basis of those of POIs and the road networks associated with the POIs. Experiments on real datasets demonstrate that our method achieves statistically significant improvements over baseline methods in terms of ranking quality in the information retrieval for roads in three areas given 25 keywords.