Multi-label categorizing local event information from micro-blogs
Wataru Yamada, Haruka Kikuchi, Keiichi Ochiai, Shu Takahashi, Yusuke Fukazawa, Hiroshi Inamura, K. Ohta · 2016
Micro-blog service Twitter holds innumerable userposted short messages called tweets that cover various topics including local events. We proposed a method to extract a mount of various local event information using natural language processing from Twitter. This paper describes a method to extract event information and label categories such as music or culture to them. Our proposal is composed of two steps: 1) extract local event information from tweets related to local event by the Support Vector Machine and Conditional Random Fields approach. 2) label categories by combining the output from classifiers of each event category. We implement the proposed method in three ways that consist of keyword matching designed by hand, machine learning and hybrid of them. Besides, we evaluate classification performance using typical five kinds of event categories. As a result, we confirmed the method of the hybrid has highest average F-score 0.674 in the methods.