Implicit Feature Detection by Ontology Aided Feature-Based Opinion Summarization - Kanbur, Dervis; Aktas, Mehmet S. (IEEE, 2017)
Thanks to e-commerce in an increasingly developing structure the number of customer feedbacks grows rapidly. Due to the great increase in the number of commerce enterprises and customers, it becomes difficult for a potential customer to read these feedbacks while decision-making. It becomes almost impossible for the producer to monitor these feedbacks, as well. Product feature extraction from customer reviews is an important sub-research area in opinion mining. The extracted features help to assess the opinions written by customers who have purchased specific products and they provide opinions of customers regarding their positive/negative experiences. Because most of customer reviews are asyntactic plain texts, methods should be developed for extraction of implicit and explicit product features expressed in customer reviews and comments. In this research, we aim at developing a system which reviews and summarizes feedbacks given in Turkish language. Our study differs from others in that it word/word groups, that it uses product features and that it combines synonym ontology including combines Turkish abbreviations/loan words and it increases the success in extraction of product features. Our test results using feedbacks of particular products on the web indicates the impact of our study.
Keywords: text mining; opinion classification; opinion mining; ontogoly; implicit feature mining; Computer Science