Mining opinionated product features using WordNet lexicographer files

• Published in: Journal of information science Vol. 43; no. 6; pp. 769 - 785
• Main Authors: Alrababah, Saif A. Ahmad, Gan, Keng Hoon, Tan, Tien-Ping
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-12-2017; Bowker-Saur Ltd
• Subjects: Customers; Data mining; Empirical analysis; Feature extraction; On-line systems; Product design; Product reviews; Product specifications; Feature extraction; lexicographer file; SentiStrength; sentiment analysis; N_gram; WordNet
• ISSN: 0165-5515; 1741-6485
• DOI: 10.1177/0165551516667651

Online customer reviews are an important assessment tool for businesses as they contain feedback that is valuable from the customer perspective. These reviews provide a significant basis on which potential customers can select the product that best meets their preferences. In online reviews, customers describe positive or negative experiences with a product or service or any part of it (i.e. features). Consumers frequently experience difficulty finding the desired product for comparison because of the massive number of online reviews. The automatic extraction of important product features is necessary to support customers in search of relevant product features. These features are the criteria that make it possible for customers to characterise different types of products. This article proposes a domain independent approach for identifying explicit opinionated features and attributes that are strongly related to a specific domain product using lexicographer files in WordNet. In our approach, N_gram analysis and the SentiStrength opinion lexicon have been employed to support the extraction of opinionated features. The empirical evaluation of the proposed system using online reviews of two popular datasets of supervised and unsupervised systems showed that our approach achieved competitive results for feature extraction from product reviews.