## 1 Introduction  
Over the last decades, the computerization of almost all areas of social life has been progressing. Increasing the computing power of computers and speeding up the Internet significantly influenced the amount of data sent and archived by enterprises from many different industries. Faced with growing piles of data, humanity has been forced to ask itself questions about possible ways to use the collected information in the most useful way. In response to such questions, more and more complex algorithms are being constructed to analyze large datasets. Only a few dozen years ago did the then technological state not allow for a full learning process to be carried out in a relatively short time for a human being. Therefore, algorithms such as neural networks have long lingered in the minds of mathematicians and computer scientists as a more theoretical than practical solution. At present, day by day, the computational capacity of modern computers is becoming more and more adequate for the needs of various types of intelligent algorithms. Companies collect a wide variety of data. However, the subjects of our work are only textual data. Events occurring in the company are most often described in the language of people and thus use text not numbers, which are much easier to interpret and understand with a calculating machine, which is a computer. Every modern company has a customer contact unit. All communication with the client, his requests, claims, ideas and comments, must be documented and archived by the company for possible later verification. In large corporations, the number of text eventsaccumulated in this way can give many employees a headache. How to use large text datasets to save time and money? There is no single correct answer to this question. The aim of the presented work is to demonstrate that text classification can effectively address numerous challenges associated with the management of extensive sets of textual data collected by enterprises from various industries. Currently, existing approaches to text-event classification often rely on human resources to assign events to their respective categories. Regardless of whether it is the customer, an employee or an external company responsible for event classification, this human involvement can introduce various issues, including additional costs, communication challenges and human errors. These problems, if left unaddressed, can lead to customer dissatisfaction. Therefore, the most effective solution is to automate the event-classification process to minimize or eliminate human involvement entirely. Section 4 attempts to present a system capable of automatically classifying texts as an improvement to customer communication processes by categorizing the content sent by the customer. The main objective of this work is to create an expert system that allows for the classification of text events from various industrial processes. The implementation of such an expert system for the automatic categorization of new events is a very complex issue. The construction of the system began with designing a process for classifying text events, and then its algorithmic and mathematical description was created. To achieve this goal, a universal system for the classification of text events was implemented, which for properly structured datasets from various industries is characterized by a satisfactory level of efficiency and quality of classification. The proposed software is a desktop system implemented with Java technology using a JavaFX library. The proposed text-classification process consists of four main steps. The first of them is the preparation of each text by building its model, and the second stage is the determination of keywords for the examined set of texts. Then, all the developed features for each of the texts from the set should be extracted in order to be able to proceed to the proper classification.