Using big data database to construct new GFuzzy text mining and decision algorithm for targeting and classifying customers. (February 2019) - Liu, Jing-Wei
Highlights: A new Chinese text classification model. Using content analysis to convert important textual information and compile it into a keyword index. A decision-making algorithm for fuzzy and grey situations is used to categorize textual data. A company's database of interactions with customers is used to construct a text mining model. Abstract: After an enterprise builds a data warehouse, it can record information related to customer interactions using structured and unstructured data. The intention is to convert these data into useful information for decision-making to ensure business continuity. Hence, this study proposes a new Chinese text classification model for the project management office (PMO) using fuzzy semantics and text mining techniques. First, content analysis is performed on the unstructured data to convert important textual information and compile it into a keyword index. Next, a classification and decision algorithm for grey situations and fuzzy (GFuzzy) is used to categorize textual data by three characteristics: maximum impact, moderate impact, and minimum impact. The purpose is to analyze consumer behaviors for the accurate classification of customers. Lastly, a more effective marketing strategy is formulated to target the various customer combinations, growth models, and the best mode of service. A company database of interactions with customers is used to construct a text mining model and to analyze the decision process of its PMO. The purpose is to test the feasibility and validity of the proposed model so that enterprises are provided with better marketing strategies and PMO processes aimed at their customers.