When tested with popular datasets, sentiment categorization using deep learning (DL) algorithms will produce positive results. Building a corpus on novel themes to train machine learning methods in sentiment classification with high assurance, however, will be difficult. This study proposes a way for representing efficient features of a dataset into a word embedding layer of DL methods in sentiment classification known as KPRO (knowledge processing and representation based on ontology), a procedure to embed knowle…