A study of vectorization methods for unstructured text documents in natural language according to their influence on the quality of work of various classifiers

The widespread increase in the volume of processed information at the objects of critical information infrastructure, presented in text form in natural language, causes a problem of its classification by the degree of confidentiality. The success of solving this problem depends both on the classifier model itself and on the chosen method of feature extraction (vectorization). It is required to transfer to the classifier model the properties of the source text containing the entire set of demarcation features as fully as possible. The paper presents an empirical assessment of the effectiveness of linear classification algorithms based on the chosen method of vectorization, as well as the number of configurable parameters in the case of the Hash Vectorizer. State text documents are used as a dataset for training and testing classification algorithms, conditionally acting as confidential. The choice of such a text array is due to the presence of specific terminology found everywhere in declassified documents. Termination, being a primitive demarcation boundary and acting as a classification feature, facilitates the work of classification algorithms, which in turn allows one to focus on the share of the contribution that the chosen method of vectorization makes. The metric for evaluating the quality of algorithms is the magnitude of the classification error. The magnitude of the error is the inverse of the proportion of correct answers of the algorithm (accuracy). The algorithms were evaluated according to the training time. The resulting histograms reflect the magnitude of the error of the algorithms and the training time. The most and least effective algorithms for a given vectorization method are identified. The results of the work make it possible to increase the efficiency of solving real practical classification problems of small-volume text documents characterized by their specific terminology.