# Hyperparameter Black-Box Optimization to Improve the Automatic Classification of Support Tickets
## 1 Introduction  
The assistance requests issued by the users to a customer service center are often called trouble or support _tickets_. These tickets generally need to be grouped by the topic of the question, so that each one is answered by an expert on the specific subject. This also aims to provide equity of treatment and prevent possible incoherence between different answers to similar questions. The effectiveness of the whole ticketing system increases when the described _semantic categorization_ of the tickets can be done automatically: besides the obvious throughput boost and economic advantages, this will also provide a more consistent and unbiased subdivision. Ticket categorization problems arise in several different areas, and can be tackled by using machine learning techniques. In the literature, these problems have been approached with a variety of techniques, more and more switching to deep learning in recent times. Quite often, the overall strategy requires a first phase to extract from the text of each ticket the _features_ describing that ticket, and then another phase to determine the partitioning. The first phase generally uses _Text Mining_, which is the branch of Data Mining concerning the process of deriving high-quality information from texts, see for details, e.g., [1]. In our case, the aim is to describe each free-form textual ticket with a standardized data record. On the other hand, the second phase can be viewed as a classification operation. Classification is the supervised process that takes a training set of elements, each of which is labeled with a class value, and learns acriterion to predict the class label of other unseen elements. In our case, it assigns a subject to each data record representing a ticket. Focusing on recent works, Ref. [2] proposes a hierarchical multi-label classification method to classify the monitoring tickets on the basis of the problem encountered. The authors introduce a contextual hierarchy loss and make also use of knowledge from the domain experts. In [3] the authors propose a multi-purpose text classifier that can tackle tasks independently of domain and language, based on hyperparameter optimization. In [4] the authors propose the use of Convolutional Neural Networks to extract features from the text of IT tickets. In [5] the authors present a whole framework for knowledge extraction from call-center data and other textual data sources such as social networks, to obtain data patterns. Work [6] proposes a Financial Ticket Classification network based on weakly supervised fine-grained classification discriminative filter learning networks. The authors use a deep Convolution Neural Network to extract highly descriptive features of the tickets, and a large-margin softmax loss function to improve the classification accuracy. In [7] the authors use specifically designed features based on linguistic representation, and then various classifiers, to predict the ticket class label of low, medium, or high complexity, showing that even simple algorithms can deliver high-quality predictions when using appropriate linguistic features. The authors of [8] classify emails in four classes, and then compare Naive Bayes, Support Vector Machines (SVMs), and K-NN, integrated with some Natural Language Processing (NLP) techniques (Stop-words removal, Stemming, and feature extraction using TF-IDF and Word2 vec). In [9] the authors present a classification of trouble tickets regarding telecommunications networks, designed to use past troubles for the resolution of the problems asked in the current trouble tickets. Finally, [10] classifies support tickets by using multiple methods of text classification and recognition, in particular by embedding documents into feature vectors and using these embeddings for finding similarities between them. In any case, the categorization of textual tickets turns out to be a particularly challenging multi-class problem.