Onto-based sentiment classification using machine learning techniques

Abstract

Sentiment analysis is a methodology used to analyse the emotion or view of an individual to a situation or topic. In present scenario, Social media is the source for the collection of individual's feedbacks, user's emotions, reviews and personal experiences which lead to a need for efficient mining of the text to derive knowledge. An optimal classification of text based on emotion is an unsolved problem in text mining. To extract knowledge from text many machine learning tools and techniques were proposed. An onto-based process is proposed to analyse the customer's emotion in this paper. The input emotional text that needs to be classified is given as input to the NLP and processed and an emotional ontology is created for better understanding of the semantics and relationships. When adding new instances, Ontology can be automatically classify them based on emotional relationship. The Emowords from ontology can be further classified using any of the standard machine learning techniques which definitively gives a better performance. This paper is a review of all the machine learning techniques that can be applied on the semantic analysis of sentiments.