title: Enhancing business intelligence by means of suggestive reviews.
abstract: Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers' choices and designers' understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons.
authors: Faculty of Computer Science and Information Technology, University of Malaya, Lembah Pantai, 50603 Kuala Lumpur, Malaysia.
authors: Atika
authors: A
authors: Qazi
authors: Faculty of Computer Science and Information Technology, University of Malaya, Lembah Pantai, 50603 Kuala Lumpur, Malaysia.
authors: Ram Gopal
authors: 0000-0002-8627-1113
authors: RG
authors: Raj
authors: Faculty of Information Science and Technology, COMSATS Institute of Information Technology (CIIT), Park Road, Islamabad 44000, Pakistan ; Faculty of Computing and Information Technology, King Abdulaziz University, North Jeddah Branch, Jeddah 21589, Saudi Arabia.
authors: Muhammad
authors: 0000-0002-1747-7265
authors: M
authors: Tahir
authors: School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798.
authors: Erik
authors: 0000-0002-3030-1280
authors: E
authors: Cambria
authors: Faculty of Business and Accountancy, University of Malaya, Lembah Pantai, 50603 Kuala Lumpur, Malaysia.
authors: Karim Bux Shah
authors: 0000-0002-9327-0979
authors: KB
authors: Syed
mesh_terms: D001185:Artificial Intelligence; D003258:Consumer Behavior; D040541:Marketing / Q000379:methods*; D013404:Suggestion; D011795:Surveys and Questionnaires