Open AccessResearch on multi-factor Identification model of acceptance content of Work order complaint
Author(s) -
Huaming Shang,
Yan Liu,
Anna Zheng,
Qi Zhou,
Ren Min
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1651/1/012136
Subject(s) - complaint , loyalty , order (exchange) , computer science , work (physics) , electricity , identification (biology) , classifier (uml) , customer satisfaction , operations research , business , marketing , artificial intelligence , engineering , mechanical engineering , botany , electrical engineering , finance , political science , law , biology
In this paper, combined with the power business demand, the purpose of this paper is to break the blind area of customers’ demand for electricity, so as to improve the management level of users’ electricity demand, and realize the mining of work order types of hot complaint business. In order to obtain the information behind the complaint work order, the power customer complaint work order is deeply mined. The weight value of keywords is calculated based on TF-IDF algorithm, and the text classifier model is constructed based on SVM classification algorithm. This study aims to provide differentiated service strategies for different types of power customers by controlling the main problems of current power customer complaints, so as to improve customer satisfaction and loyalty.