Semantics Graph Mining for Topic Discovery and Word Associations

Big Data creates many challenges for data mining experts, in particular in getting meanings of text data. It is beneficial for text mining to build a bridge between word embedding process and graph capacity to connect the dots and represent complex correlations between entities. In this study we examine processes of building a semantic graph model to determine word associations and discover document topics. We introduce a novel Word2Vec2Graph model that is built on top of Word2Vec word embedding model. We demonstrate how this model can be used to analyze long documents, get unexpected word associations and uncover document topics. To validate topic discovery method we transfer words to vectors and vectors to images and use CNN deep learning image classification.

Detect Text Topics by Semantics Graphs

It is beneficial for document topic analysis to build a bridge between word embedding process and graph capacity to connect the dots and represent complex correlations between entities. In this study we examine processes of building a semantic graph model, finding document topics and validating topic discovery. We introduce a novel Word2Vec2Graph model that is built on top of Word2Vec word embedding model. We demonstrate how this model can be used to analyze long documents and uncover document topics as graph clusters. To validate topic discovery method we transfer words to vectors and vectors to images and use deep learning image classification.