Paper Info
Title
Multi-label categorizing local event information from micro-blogs
Abstract
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.
Year
DOI
Venue
2016
10.1109/ICMU.2016.7742090
2016 Ninth International Conference on Mobile Computing and Ubiquitous Networking (ICMU)
Keywords
Field
DocType
multilabel categorizing local event information,micro-blog service,Twitter,short messages,tweets,natural language processing,event information extract,support vector machine,conditional random field approach,keyword matching designed,machine learning,classification performance,average F-score
Conditional random field,Training set,Mobile computing,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-5090-1742-3
0
0.34
References 
Authors
6
7
Name
Order
Citations
PageRank
Wataru Yamada12915.22
Haruka Kikuchi2324.06
Keiichi Ochiai373.21
Shu Takahashi400.34
Yusuke Fukazawa513719.28
Hiroshi Inamura625325.67
Ken Ohta7285.21