Recently, automatic classification of IT tickets has gained notable attention due to the increasing complexity of IT services deployed in enterprises. There are multiple discussions and no general opinion in the research and practitioners' community on the design of IT ticket classification tasks, specifically the choice of ticket text representation techniques and classification algorithms. Our study aims to investigate the core design elements of a typical IT ticket text classification pipeline. In particular, we compare the performance of TF-IDF and linguistic features-based text representations designed for ticket complexity prediction. We apply various classifiers, including kNN, its enhanced versions, decision trees, naïve Bayes, logistic regression, support vector machines, as well as semi-supervised techniques to predict the ticket class label of low, medium, or high complexity. Finally, we discuss the evaluation results and their practical implications. As our study shows, linguistic representation not only proves to be highly explainable but also demonstrates a substantial prediction quality increase over TF-IDF. Furthermore, our experiments evidence the importance of feature selection. We indicate that even simple algorithms can deliver high-quality prediction when using appropriate linguistic features.

Purpose: This study aims to draw the attention of business process management (BPM) research and practice to the textual data generated in the processes and the potential of meaningful insights extraction. The authors apply standard natural language processing (NLP) approaches to gain valuable knowledge in the form of business process (BP) complexity concept suggested in the study. It is built on the objective, subjective and meta-knowledge extracted from the BP textual data and encompassing semantics, syntax and stylistics. As a result, the authors aim to create awareness about cognitive, attention and reading efforts forming the textual data-based BP complexity. The concept serves as a basis for the development of various decision-support solutions for BP workers.Design/methodology/approach: The starting point is an investigation of the complexity concept in the BPM literature to develop an understanding of the related complexity research and to put the textual data-based BP complexity in its context. Afterward, utilizing the linguistic foundations and the theory of situation awareness (SA), the concept is empirically developed and evaluated in a real-world application case using qualitative interview-based and quantitative data-based methods.Findings: In the practical, real-world application, the authors confirmed that BP textual data could be used to predict BP complexity from the semantic, syntactic and stylistic viewpoints. The authors were able to prove the value of this knowledge about the BP complexity formed based on the (1) professional contextual experience of the BP worker enriched by the awareness of cognitive efforts required for BP execution (objective knowledge), (2) business emotions enriched by attention efforts (subjective knowledge) and (3) quality of the text, i.e. professionalism, expertise and stress level of the text author, enriched by reading efforts (meta-knowledge). In particular, the BP complexity concept has been applied to an industrial example of Information Technology Infrastructure Library (ITIL) change management (CHM) Information Technology (IT) ticket processing. The authors used IT ticket texts from two samples of 28,157 and 4,625 tickets as the basis for the analysis. The authors evaluated the concept with the help of manually labeled tickets and a rule-based approach using historical ticket execution data. Having a recommendation character, the results showed to be useful in creating awareness regarding cognitive, attention and reading efforts for ITIL CHM BP workers coordinating the IT ticket processing.Originality/value: While aiming to draw attention to those valuable insights inherent in BP textual data, the authors propose an unconventional approach to BP complexity definition through the lens of textual data. Hereby, the authors address the challenges specified by BPM researchers, i.e. focus on semantics in the development of vocabularies and organization- and sector-specific adaptation of standard NLP techniques.

Nowadays, enterprises need to handle a continually growing amount of text data generated internally by their employees and externally by current or potential customers. Accordingly, the attention of managers shifts to an efficient usage of this data to address related business challenges. However, it is usually hard to extract the meaning out of unstructured text data in an automatic way. There are multiple discussions and no general opinion in the research and practitioners’ community on the design of text classification tasks, specifically the choice of text representation techniques and classification algorithms. One essential point in this discussion is about building solutions that are both accurate and understandable for humans. Being able to evaluate the classification decision is a critical success factor of a text classification task in an enterprise setting, be it legal documents, medical records, or IT tickets. Hence, our study aims to investigate the core design elements of a typical text classification pipeline and their contribution to the overall performance of the system. In particular, we consider text representation techniques and classification algorithms, in the context of their explainability, providing ultimate insights from our IT ticket complexity prediction case study. We compare the performance of a highly explainable text representation technique based on the case study tailored linguistic features with a common TF-IDF approach. We apply interpretable machine learning algorithms such as kNN, its enhanced versions, decision trees, naïve Bayes, logistic regression, as well as semi-supervised techniques to predict the ticket class label of low, medium, or high complexity. As our study shows, simple, explainable algorithms, such as decision trees and naïve Bayes, demonstrate remarkable performance results when applied with our linguistic features-based text representation. Furthermore, we note that text classification is inherently related to Granular Computing.