Entity Association Mining.

Abstract

Newspaper is an important part of human life since the old times. First thing we do in the morning is to go through the newspaper. Everyone has their own way of keeping their self-informed about what is happening in the world. But all those efforts and struggles to keep in touch with what is happening around world are in vain until they do not know that about those people/events/ organization/country they are reading, are they associated with each other? In this busy world, no one has time to go through the news articles with the association perspective. But there is no platform available to analyze news articles and associate entities with respect to information stated in news articles. EAM is a web platform for journalism community and those users who are interested in finding out association. The NEWS articles are extracted from five different international NEWS resources through web scraping, then it is passed through NLP pipeline to extract information from extracted NEWS articles. Then, entities are extracted from the extracted entities that are then is then normalized as per the occurrence in NEWS article which is then passed to FP-Growth algorithm that gives frequent item sets as output that is passed to association rules function to extract rules as association between entities, the output is decision matrix. Then the graph is generated from those extracted rules.