A Framework for Online Social Network Volatile Data Analysis: A Case for the Fast Fashion Industry
Anoud Bani Hani (Zayed University, United Arab Emirates)
Feras Al-Obeidat (Zayed University, United Arab Emirates)
Elhadj Benkhelifa (email@example.com, United Kingdom)
Oluwasegun Adedugbe (Staffordshire University, United Kingdom)
Abstract: Consumer satisfaction is an important part for any business as it has been shown to be a major factor for consumer loyalty. Identifying satisfaction in products is also important as it allows businesses alter production plans based on the level of consumer satisfaction for a product. With consumer satisfaction data being very volatile for some products due to a short requirement period for such products, current consumer satisfaction must be identified within a shorter period before the data becomes obsolete. The fast fashion industry, which is part of the fashion industry, is adopted as a case study in this research. Unlike slow fashion, fast fashion products have short shelf lives and their retailers must be able to react swiftly to consumer demands. This research aims to investigate the effectiveness of current data mining techniques when used to identify consumer satisfaction towards fast fashion products. This is carried out by designing, implementing and testing a software artefact that utilises data mining techniques to obtain, validate and analyse fast fashion social data, sourced from Twitter, to identify consumer satisfaction towards specific product types. In addition, further analysis is carried out using a sentiment scaling method adapted to the characteristics of fast fashion.
Keywords: consumer satisfaction, data mining, fast fashion, sentiment analysis, sentiment scaling, social mining
Categories: K.4, K.7, K.8