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Neural Potential Learning for Tweets Classification and Interpretation

机译：神经潜力学习推文分类和解释

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摘要

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.
机译：本文旨在将新的神经学习方法应用于“神经势能学习，NPL”的新神经学习方法，对推文的分类和解释。众所周知，社交媒体，如Twitter在自然灾害时传输重要信息的关键作用。特别是，自2011年大东日本地震以来，推特被认为是最有效和方便的沟通工具之一。但是，由于推文中包含了很多冗余信息，因此通常难以从推文的流量中获取重要信息。因此，迫切需要开发一些方法来从冗余推文中提取重要信息和有用的信息。为了应对复杂和冗余数据，开发了一种新的神经势学习来提取重要信息。该方法旨在找到一些高度潜在的神经元，尽可能地增强这些神经元，以减少冗余信息并专注于重要信息。该方法应用于地震中收集的真实推文数据，发现该方法可以比其他传统机器学习方法更准确地将推文作为重要和不重要的方法对。此外，该方法可以根据对高潜在神经元的检查来解释如何分类推文。

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