The opinions expressed by the consumers on online product reviews in e-commerce websites play major role in judging the evaluative character of the product aspect. These expressed opinions lack conceptual preciseness allowing consumers to use them in both syntactically and semantically different ways (lexical variations) on various aspects in the reviews. Also some section of consumers present their opinions in the implicit manner. The evaluation of these types of opinions for opinion orientations raises the semantic gap between the human language and the actual opinionated knowledge. Thus, extracting all these types of opinions on the product aspects may bridge the semantic gap and thereby improving the accuracy of the opinion orientation. In this paper, iterative ontology learning approach is carried out in order to solve the aforementioned problems. In the proposed method, first the pre-processed product reviews are analyzed for extracting opinionated lexical variations. Then, the reviews are further analyzed to extract the implicit opinions. Further, these opinionated lexical variations and implicit opinions with the reviews are formalized for ontology learning. The aspect, opinion pair is formed by reasoning the learned ontology. Finally, the aspect’s opinion orientation is ascertained by using the sentiwordnet scores in the improved geodesic distance metric. The evaluation of semantic orientation of opinions using the learned ontology guidance against the state-of-the-art approaches shows the effectiveness of the proposed method.
online reviews, product aspects, opinions, adjective, lexical variations, implicit opinions, ontology learning, semantic orientation
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