Development of an Algorithm for Automatic Analysis of Sentiment in School Essays of the Uzbek Language

Abstract

In this study, the authors presented an algorithm for automatic sentiment analysis in school essays written in the Uzbek language. The algorithm is implemented on the basis of a on a convolutional neural network architecture, designed to classify text using TensorFlow and Keras. Authors created a training dataset consisting of almost 5000 sentences and phrases, most commonly used in everyday communication. The text data underwent preprocessing, including punctuation removal and conversion to lowercase, before being transformed into numerical representations using an embedding layer that was trained simultaneously with the model. Besides, the authors tested the effectiveness of the model, where the evaluation was carried out using such metric as precision. As a result of testing, the precision reached 88 for sentiment analysis in 50 essays, which consist of 811 sentences overall. Moreover, the authors conducted a comparative analysis of existing works and proposed further options for the development of the algorithm.

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