Neural potential learning for tweets classification and interpretation

Abstract

The present paper aims to apply a new neural learning method called "Neural Potential Learning, NPL" to the classification and interpretation of tweets. It has been well known that social media such as the Twitter play crucial roles in transmitting important information at the time of natural disasters. In particular, since the Great East Japan Earthquake in 2011, the Twitter has been considered as one of the most efficient and convenient communication tools. However, because much redundant information is contained in the tweets, it is usually difficult to obtain important information from the flows of the tweets. Thus, it is urgently needed to develop some methods to extract the important and useful information from redundant tweets. To cope with complex and redundant data, a new neural potential learning has been developed to extract the important information. The method aims to find some highly potential neurons and enhance those neurons as much as possible to reduce redundant information and to focus on important information. The method was applied to the real tweets data collected in the earthquake and it was found that the method could classify the tweets as important and unimportant ones more accurately than the 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 neurons.