الفهرس 
An enhanced opinion retrieval approach for customer services expansion
Abstract EnglishOnly 14 pages are availabe for public view
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Abstract
Recently, e-commerce is considered as a vital environment for online shopping services. Usually, products might be offered by several suppliers and different services are expected by customers, which make it difficult to develop products that can fulfill their expectation. Therefore, the majority of developers recently extend the existing system functionalities to keep up the competitiveness among suppliers and to recognize what actually their customers{u2019} needs through their products{u2019} reviews. The need for more innovative requirements becomes an essential task. With the huge amount of reviews posted daily, it becomes impossible for companies to read and analyze it manually and recognize what customers need. Moreover, insufficient requirement analysis might lead to a tendency to a specific view. Therefore, opinion retrieval becomes a demanding task for automatically extracting, analyzing and summarizing these reviews. Usually, the retrieved results efficiency is affected by the extracted features which may be stated explicitly or written in an implicit manner which affects the recall results. The majority of feature extraction and retrieval studies are focused on the explicit type that are tailored for specific languages, while the implicit type did not receive much attention