The article is devoted to neural network text classification algorithms. The relevance of this topic is due to the ever-growing volume of information on the Internet and the need to navigate it. In this paper, in addition to the classification algorithm, a description is also given of the methods of text preprocessing and vectorization, these steps are the starting point for most NLP tasks and make neural network algorithms efficient on small data sets. In the work, a sampling of 50,000 English IMDB movie reviews will be used as a dataset for training and testing the neural network. To solve this problem, an approach based on the use of a convolutional neural network was used. The maximum achieved accuracy for the test sample was 90.16%.

text comprehension, natural language processing, convolutional neural networks, text classification

Popova Ekaterina Sergeevna,  ,  National Research Tomsk Polytechnic University

Spitsyn Vladimir Grigorievich,  ,  National Research Tomsk Polytechnic University

Ivanova Yulia Aleksandrovna,  ,  National Research Tomsk Polytechnic University