Document Classification by Order of Context, Concept and Semantic Relations: OCCSR

Abstract

The contemporary study in text or document mining is focusing on syntactic components and the semantic environment. In order to accomplish this, and with the motivation gained from our previous research contributions, we investigated a mining model to classify documents based on the Order of Context, Concept, and Semantic Relations (OCCSR). This proposed model categorises documents into three levels: context, concept, and semantic. The document context is defined by the meta-data in the document, the concept is defined by the order of features, and semantic relations are assessed by correlating the activities observed in the documents. The experimental results show that the OCCSR has high classification accuracy,  scalable, and  robust. The study findings lead us to the conclusion that context similarity, in addition to concept and semantic similarity, is more important in achieving classification accuracy in supervised learning. The OCCSR is evaluated using a confusion matrix and discriminator metrics. The model developed here is extremely useful, particularly for assessing the relationship of documents published in social communities such as electronic journals, publishers, and blogs.