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    Justifying the selection of a neural network linguistic classifier
    (ХНУРЕ, 2023) Barkovska, O.; Voropaieva, K.; Ruskikh, O.
    The subject matter of this article revolves around the exploration of neural network architectures to enhance the accuracy of text classification, particularly within the realm of natural language processing. The significance of text classification has grown notably in recent years due to its pivotal role in various applications like sentiment analysis, content filtering, and information categorization. Given the escalating demand for precision and efficiency in text classification methods, the evaluation and comparison of diverse neural network models become imperative to determine optimal strategies. The goal of this study is to address the challenges and opportunities inherent in text classification while shedding light on the comparative performance of two well-established neural network architectures: Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). To achieve the goal, the following tasks were solved: a comprehensive analysis of these neural network models was performed, considering several key aspects. These aspects include classification accuracy, training and prediction time, model size, data distribution, and overall ease of use. By systematically assessing these attributes, this study aims to provide valuable information about the strengths and weaknesses of each model and enable researchers and practitioners to make informed decisions when selecting a neural network classifier for text classification tasks. The following methods used are a comprehensive analysis of neural network models, assessment of classification accuracy, training and prediction time, model size, and data distribution. The following results were obtained: The LSTM model demonstrated superior classification accuracy across all three training sample sizes when compared to CNN. This highlights LSTM's ability to effectively adapt to diverse data types and consistently maintain high accuracy, even with substantial data volumes. Furthermore, the study revealed that computing power significantly influences model performance, emphasizing the need to consider available resources when selecting a model. Conclusions. Based on the study's findings, the Long Short-Term Memory (LSTM) model emerged as the preferred choice for text data classification. Its adeptness in handling sequential data, recognizing long-term dependencies, and consistently delivering high accuracy positions it as a robust solution for text analysis across various domains. The decision is supported by the model's swift training and prediction speed and its compact size, making it a suitable candidate for practical implementation.