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September 25, 2023 to September 29, 2023 - Porto, Portugal

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TrendNews 2023 Congress
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November 13, 2023 to November 17, 2023 - Valencia, Spain

SoftNet 2023 Congress
November 13, 2023 to November 17, 2023 - Valencia, Spain

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IARIA Congress 2023, The 2023 IARIA Annual Congress on Frontiers in Science, Technology, Services, and Applications
November 13, 2023 to November 17, 2023 - Valencia, Spain

 

 


ThinkMind // WEB 2017, The Fifth International Conference on Building and Exploring Web Based Environments // View article web_2017_1_20_40027


Ontology Based Aspect Oriented Opinion Summary Methodology

Authors:
Dervis Kanbur
Mehmet S. Aktas

Keywords: text mining; sentiment classification; review summarizing; synonym word group; ontology;

Abstract:
Enterprises selling over the web generally request feedbacks from customers on their purchased products/services. 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 e- commerce enterprises and customers, it becomes difficult for a potential customer to read these feedbacks while making decisions. It becomes almost impossible for the producer to monitor these feedbacks as well. Product feature extraction from customer reviews is an important operation 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 the 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 to develop a methodology that examines the opinions, thoughts and comments, written in Turkish, about the products and summarizes the features of the products and the emotional processes related to these properties. Our study differs from others in that product characteristics expressed using synonyms or word groups can be identified, and features can be detected with greater accuracy using ontologies that include product specifications. Our study offers successful results in product feature extraction. Experimental work on opinion/idea/comment for some products on e-commerce sites shows that our methodology yields positive results.

Pages: 7 to 16

Copyright: Copyright (c) IARIA, 2017

Publication date: May 21, 2017

Published in: conference

ISSN: 2308-4421

ISBN: 978-1-61208-557-9

Location: Barcelona, Spain

Dates: from May 21, 2017 to May 25, 2017

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