Classification of Customer Demands by Using Doc2Vec Feaure Extraction Method

Companies provide after-service communication with their customers through help desk systems they provide in call centers and websites. In companies with large networks, the amount of data collected by using these communication tools is increasing day by day.The separation of these collected requests and feedbacks to be transferred to the relevant units has become a very time consuming process. The prolongation of this process in customer-oriented companies can lead to customer loss.Therefore, it has importance to transfer, evaluate and return requests from such firms. In this study, firstly, two different models were used to obtain the features from the demands of customers. The features obtained were classified by multi-layer artificial neural network and it was provided the related demand was transferred to the related unit. Thus, the usability of the Doc2Vec model, which can be used as an alternative to the classic word bag, is examined in Turkish text classification studies.