Ontology learning on product reviews to extract aspects and opinions

Abstract

More number of online product reviews, into e-commerce database, from time to time on a daily basis are produced. In order to analyze huge number of reviews for aspects and opinions is a complex task. This is because these reviews that are produced from time to time are not properly structured and there is a lot of fancying in the literature. This often makes the language, unstructerd and thus makes it difficult to analyze. ‘NLP’, which means the Natural language processing and Ontology learning techniques are used to automate these tasks. The semantic gap (gap between written reviews and the actual knowledge) was observed when aspects and opinions are extracted through these techniques. The original Ontology learning (OL) reduces this gap. Maximum number of aspects and opinions extraction is estimated using OL. These aspects and opinions can be found individually or in pairs.