# Systematic Comparison of Vectorization Methods in Classification Context
## 1 Introduction  
Natural language processing has been the subject of numerous studies in the last decade. These have focused on the various stages of text processing, from text preparation to vectorization to final text comprehension. The goal of vector space modeling is to project words in a language corpus into a vector space in such a way that words that are similar in meaning are close to each other. Currently, there are two approaches to the topic of vectorization. The first one focuses on placing words in the context of the language, while the second one deals with full documents and the representation of single sentences and paragraphs in the context of the documents under consideration.  
Automatic text processing is a rapidly growing field of science. The area of knowledge related to this topic is mainly concerned with data mining, which is an extremely popular and important field of computer science these days. In many languages from around the world, starting with English, research has already gone very far. Each stage of text processing is analyzed in turn, starting from how the training data are selected to the choice of the appropriate method for analyzing the text [1, 2].  
The classification of written statements is one of the operations that allow us to talk about intelligent text analysis, and at the same time, it is so simple that it is ideal as a marker for the operation of other stages of text processing. Any written statement can be analyzed, including casual speech, a message from an information service, as well as a scientific publication. Each type of text poses different challenges for automatic text analysis tools.  
This paper collects the most popular text vectorization methods from both approaches to modeling the vector space of texts, explains the deep machine learning mechanismsbehind them, and creates a summary of them based on a study of the classification performance of texts previously vectorized by the methods studied. In this paper, the main focus of the analyses is on the final task of the prepared text vectors, namely, to understand them. Therefore, it was decided to use classification as a determinant of the effectiveness of the used methods. What is more, the simplest of the methods was used for classification to avoid the influence of the choice of the method itself on the final result. The experiments conducted provide a basis for further research for better automatic text analysis.  
The main objective of this paper is to compare different existing text vectorization methods in natural language processing. The comparison of text vectorization methods is possible by checking the accuracy of classification.  
The paper is structured as follows: Section 2 focuses on selected technical issues related to automatic text processing. Section 3 describes the dataset on which the experiments were performed--it includes information such as set counts and text length histograms, and describes the preparation of the data for further automated processing steps. Section 4 presents the results of the conducted research, while Section 5 compares these results with the available literature.