Augmenting feature model through customer preference mining by hybrid sentiment analysis |
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| Affiliation: | 1. The Department of Industrial and Manufacturing Systems Engineering, The University of Michigan, Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128 USA;2. The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA;3. The Department of Industrial and Operations Engineering, The University of Michigan, Ann Arbor, 500 S State St, Ann Arbor, MI 48109 USA;4. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen,518060 China;1. Biometrics Research Center, Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China;2. Department of Computer and Information Science, University of Macau, Macau, China;3. Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China;1. DeustoTech – Computing, University of Deusto, Avenida de las Universidades 24, Bilbao 48007, Spain;2. Department of Computing Science, Umeå University, SE-901 87 Umeå, Sweden;1. School of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;2. Beijing Key Laboratory of Computational Intelligence & Intelligent System, Beijing 100124, China\n;3. Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China;4. Beijing Laboratory for Urban Mass Transit, Beijing 100124, China;5. Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia;1. Instituto de Física de São Carlos, Universidade de São Paulo, Av. Trabalhador São-Carlense, 400, Parque Arnold Schimidt, 13566-590, São Carlos - SP, Brazil;2. Federal University of Mato Grosso do Sul, Rua Itibiré Vieira, s/n, Residencial Julia Oliveira Cardinal, 79907-414, Ponta Porã - MS, Brazil;3. Instituto de Ciências Matemáticas e Computação, Universidade de São Paulo, Av. Trabalhador São-Carlense, 400, Centro, 13566-590, São Carlos - SP, Brazil;1. Department of CSE, Indian Institute of Technology Roorkee, India\n;2. Department of ECE, Institute of Engineering & Management, Kolkata, India;3. CVPR Unit, Indian Statistical Institute, Kolkata, India;1. Department of Engineering and Technological Research, Universidad Nacional de La Matanza, Argentina;2. Department of Computer Science, Universidad Católica del Maule, Chile;3. Facultad de Informática, Universidad Complutense de Madrid, Spain |
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| Abstract: | A feature model is an essential tool to identify variability and commonality within a product line of an enterprise, assisting stakeholders to configure product lines and to discover opportunities for reuse. However, the number of product variants needed to satisfy individual customer needs is still an open question, as feature models do not incorporate any direct customer preference information. In this paper, we propose to incorporate customer preference information into feature models using sentiment analysis of user-generated online product reviews. The proposed sentiment analysis method is a hybrid combination of affective lexicons and a rough-set technique. It is able to predict sentence sentiments for individual product features with acceptable accuracy, and thus augment a feature model by integrating positive and negative opinions of the customers. Such opinionated customer preference information is regarded as one attribute of the features, which helps to decide the number of variants needed within a product line. Finally, we demonstrate the feasibility and potential of the proposed method via an application case of Kindle Fire HD tablets. |
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