Feature based Sentiment Analysis using a Domain Ontology
نویسندگان
چکیده
With the increase of unstructured social media data, sentiment analysis can be applied to infer useful information to assist organizations and their customers. We propose a model for feature-based sentiment analysis using ontology to address queries like:“which car is more comfortable?, which car has better performance and interior?”. Feature based sentiment analysis is done using SentiWordNet with word sense disambiguation and an ontology that is developed by us. Data Tables are prepared from the RDF triples of parsed ontology and the sentiment ranks of car attributes. To relate the RDBM data to the built ontology of car, mapping axioms are proposed to connect them using OBDA model. Using SPARQL query, the results of the proposed model are compared with a dictionary-based method with respect to different car attributes. The performance of our model is better than dictionary based method.
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