Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Development of an Algorithm for Automatic Analysis of Sentiment in School Essays of the Uzbek Language. / Mengliev, Davlatyor B.; Abdurakhmonova, Nilufar Z.; Barakhnin, Vladimir B. et al.
Proceedings of the IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024. Institute of Electrical and Electronics Engineers Inc., 2024. p. 1570-1573.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - Development of an Algorithm for Automatic Analysis of Sentiment in School Essays of the Uzbek Language
AU - Mengliev, Davlatyor B.
AU - Abdurakhmonova, Nilufar Z.
AU - Barakhnin, Vladimir B.
AU - Iskandarova, Aybibi R.
AU - Topildiyeva, Feruza R.
AU - Akhmedov, Ergash Yu
N1 - Conference code: 3
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Uzbek language
KW - convolutional neural networks
KW - custom model
KW - emotion detection
KW - essay analysis
KW - low-resource languages
KW - named entity recognition
KW - sentiment analysis
KW - text classification
KW - text processing
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85216576277&origin=inward&txGid=9927e208f29712563c39825f107a72f2
UR - https://www.mendeley.com/catalogue/92ef6208-3e9a-389e-9ea2-b97278655ad8/
U2 - 10.1109/PIERE62470.2024.10804909
DO - 10.1109/PIERE62470.2024.10804909
M3 - Conference contribution
SN - 979-8-3315-1633-8
SP - 1570
EP - 1573
BT - Proceedings of the IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Problems of Informatics, Electronics and Radio Engineering
Y2 - 15 November 2024 through 17 November 2024
ER -
ID: 64588026