An enhanced opinion retrieval approach via implicit feature identification
- PDF / 3,914,441 Bytes
- 26 Pages / 439.642 x 666.49 pts Page_size
- 101 Downloads / 198 Views
An enhanced opinion retrieval approach via implicit feature identification Sarah Saad Eldin1
· Ammar Mohammed1,2 · Ahmed Sharaf Eldin1 · Hesham Hefny2
Received: 23 January 2020 / Revised: 17 September 2020 / Accepted: 17 September 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Recently, there has been an enormous increase in the number of reviews of popular products. Therefore, opinion analysis has become a tedious task for customers when making decisions. As a result, opinion retrieval systems have emerged as an effective tool to analyze and represent customers’ feelings toward offered services. Conventional opinion retrieval systems retrieve and rank products according to both relevance and the overall polarity scores of the opinions. However, customer reviews are usually more detailed, including multiple features with different polarities. Consequently, feature-based opinion retrieval is necessary to extract and analyze each feature separately. Customers’ opinions are usually written with a short and unclear structure and contain many implicit linguistic features that cannot be identified by retrieval systems. As a result, the recall results are negatively affected. Few studies have focused on implicit features, as most examined explicit features. Also, implicit features extraction is a challenging task in some languages like Arabic due to difficulties with morphology. This paper proposes an enhanced retrieval approach based on feature-based opinion mining to enhance retrieval performance. In addition to explicit feature extraction, a metaheuristic optimization method with several similarity measures is utilized to identify implicit features and measure its effect on the retrieval results. The experimental results on Arabic and English datasets revealed the effectiveness of the proposed approach, whereby more features were extracted compared to the explicit feature results. Furthermore, the ranking results were improved by identifying both implicit and explicit features compared to the results obtained by the conditional random field method and association rule mining. Keywords Features based opinion mining · Feature sentiment analysis · Opinion retrieval · Implicit feature extraction · Product features extraction Sarah Saad Eldin
[email protected] Ammar Mohammed [email protected] 1
Department of Computer Science, Faculty of Graduate Studies for statistical Researches, Cairo University, Cairo, Egypt
2
Department of Computer Science, Faculty of Computer Science, Misr International University, Cairo, Egypt
Journal of Intelligent Information Systems
1 Introduction Due to the rapid increase in the number of online products, customers have become increasingly interested in expressing their opinions about features of the products. These opinions are considered valuable information resources that can affect the purchasing decisions of other customers and reflect customers’ feedback, which can be used to enhance product services according to customers’ perspectives. Howev
Data Loading...
