文章基本信息

标题：Potential Information Maximization: Potentiality-Driven Information Maximization and Its Application to Tweets Classification and Interpretation
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作者：Ryozo Kitajima ; Ryotaro Kamimura ; Osamu Uchida 等
期刊名称：International Journal of Computer Information Systems and Industrial Management Applications
印刷版ISSN：2150-7988
电子版ISSN：2150-7988
出版年度：2016
卷号：8
页码：042-051
出版社：Machine Intelligence Research Labs (MIR Labs)
摘要：The present paper aims to apply a new information- theoretic learning method called "potential information maxi- mization" to the classification and interpretation of tweets. It is well known that social media sites such as Twitter play a cru- cial role in transmitting important information during natural disasters. In particular, since the Great East Japan Earthquake in 2011, Twitter has been considered as one of the most efficien- t and convenient communication tools. However, since there is much redundant information contained in tweets, it is critical that methods be developed to extract only the most important information from them. To cope with complex and redundant data, a new neural information-theoretic learning method has been developed for this purpose. The method aims to find neu- rons with high potential and maximize their information con- tent to reduce redundancy and to focus on important informa- tion. The method was applied to real tweet data collected dur- ing the earthquake. It was found that the method could classify the tweets as important and unimportant more accurately than other conventional machine learning methods. In addition, the method made it possible to interpret how the tweets could be classified based on the examination of highly potential neuron- s.
关键词：twitter; classification; interpretation; neural network; ; potential information