Analysis of Life Context of On-Line Group-Buying Population by Dynamic Decision

While it is difficult to avoid uncertainties when shopping on the Internet, trust can reduce customers’ perceived uncertainties, and enhance their willingness and frequency to buy products and services. The difference in time and space information transparency between customers and on-line sellers, as well as the complex unpredictability of network structure, result in frequent uncertainty for on-line transactions. Therefore, through text mining and integrating the Genetic Algorithm (GA) with the Support Vector Machine (SVM), this project classifies the data of on-line group buying community complaints according to the posts left on Facebook and the three major group-buying websites of Taiwan. The terms are selected based on term frequency, document frequency, uniformity, and conformity, while document classification effectiveness is calculated using precision, recall rate, and F-measure. Community complaints are classified into the uncertain performance indicators that influence on-line group buying for integrated statistics, in order that specific performance indicators of community group-buying websites can be generated. Afterwards, based on the on-line group buying community performance indicator sequence, as integrated according to the dynamic Multicriteria Optimization and Compromise Solution (VIKOR) method and prosperity countermeasure signals, grey correlation sorting is applied to analyze the dynamic performance indicator sequence of different communities, in order to determine the life context of different populations for the reference of on-line group buying providers.