Ket Yee Lee, RHRZ@RPTU and University of Applied Sciences Kaiserslautern

Title: Data Preprocessing Using NLP for Ticket Dispatching with Classical Classification Methods

Abstract:

Writing a support request from a customer perspective can be challenging. From a data science point of view, processing them for machine learning can be challenging as well. In this master thesis, the methods from Natural Language Processing (NLP) are applied to prepare the assignment of service requests into the respective associated queues in a machine-understandable way. Texts are cleaned and qualified using tokenization and part-of-speech tagging (POS) with the German Stuttgart-Tübingen tag set (STTS).
The classical classification methods Naïve Bayes, Decision Tree, Logistic Regression, Super Vector Machine, k Nearest Neigbor and the ensemble methods Random Forest, Gradient Boosting and Tree Ensemble are contrasted.
Dispatching of customer queries written in natural language is possible using classical classification methods. The proof-of-concept shows that with Logistic Regression, for the available database, tickets can be assigned by machine with 64.5% accuracy for the top 10 queues.