Crucial data like product features were obtained from consumer online reviews and sentiment words were gathered in Resource Description Format RDF in order to use them in meaningful reviews based categorisation on sentiments of the feature. The meaningful relationships among these pieces of RDF data are to be engineered in a Product Review Opinion Ontology PROO. This serves as background knowledge to learn rule based sentiments expressed on product features. These semantic rules are learned on both taxonomical and non-taxonomical relations available in PROO Ontology. In order to verify the mined rules, Inductive Logic Programming ILP is applied on PROO. The learned ILP rules are found to be among the mined rules. The positively classified features are grouped to justify the goal of ILP for examples which are both complete and consistent. Left out negative examples are useful in knowing their count at the time of categorisation.
['D. Teja Santosh', 'B. Vishnu Vardhan']
PROO ontology development for learning feature specific sentiment relationship rules on reviews categorisation: a semantic data mining approach