Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Micro-blog service Twitter holds innumerable userposted short messages called tets that cover various topics including local events.  proposed a method to extract a mount of various local event information using natural language processing from Twitter.  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 tets 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.  implement the proposed method in three ways that consist of keyword matching designed by hand, machine learning and hybrid of them. Besides,  evaluate classification performance using typical five kinds of event categories. As a result,  confirmed the method of the hybrid has highest average F-score 0.674 in the methods.
['Wataru Yamada', 'Haruka Kikuchi', 'Keiichi Ochiai', 'Shu Takahashi', 'Yusuke Fukazawa', 'Hiroshi Inamura', 'Ken Ohta']
I am writing a research paper on a idea Multi-label categorizing local event information from micro-blogs
[{"from": "human", "value": "I am writing a research paper on a idea Multi-label categorizing local event information from micro-blogs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Micro-blog service Twitter holds innumerable userposted short messages called tets that cover various topics including local events.  proposed a method to extract a mount of various local event information using natural language processing from Twitter.  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 tets 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.  implement the proposed method in three ways that consist of keyword matching designed by hand, machine learning and hybrid of them. Besides,  evaluate classification performance using typical five kinds of event categories. As a result,  confirmed the method of the hybrid has highest average F-score 0.674 in the methods."}]