Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Enhancing Sentiment Analysis in Uzbek Language Texts through Weighted Lexical Features. / Mengliev, Davlatyor; Abdurakhmonova, Nilufar; Barakhnin, Vladimir et al.
International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM. IEEE Computer Society, 2024. p. 2450-2453 (International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - Enhancing Sentiment Analysis in Uzbek Language Texts through Weighted Lexical Features
AU - Mengliev, Davlatyor
AU - Abdurakhmonova, Nilufar
AU - Barakhnin, Vladimir
AU - Vasliddinova, Kamola
AU - Rahimov, Hasanboy
AU - Djalolova, Kamola
N1 - Conference code: 25
PY - 2024
Y1 - 2024
N2 - This article presents an original sentiment analysis algorithm developed for analyzing Uzbek language texts in order to correctly determine and identify the sentiment of the text. The method proposed by the authors provides a rating system where lexical characteristic weights are implemented for each word, phrase, idiom or negation. The proposed system involves marking words with sentiment scores in a numerical range, where the minimum value is -3 and the maximum is +3. The authors also tested the efficiency of the algorithm, where the result showed a high accuracy, and the completeness of the identified objects is in the range from 85% to 95%, depending on the type of object (word, phrase, negation, etc.). Despite the positive test results, the evaluation process also identified areas for improving the algorithm, including expanding the algorithm's vocabulary for the highest quality processing of word forms. In addition, authors noted about alternative technologies that might be used for enhancing current algorithm.
AB - This article presents an original sentiment analysis algorithm developed for analyzing Uzbek language texts in order to correctly determine and identify the sentiment of the text. The method proposed by the authors provides a rating system where lexical characteristic weights are implemented for each word, phrase, idiom or negation. The proposed system involves marking words with sentiment scores in a numerical range, where the minimum value is -3 and the maximum is +3. The authors also tested the efficiency of the algorithm, where the result showed a high accuracy, and the completeness of the identified objects is in the range from 85% to 95%, depending on the type of object (word, phrase, negation, etc.). Despite the positive test results, the evaluation process also identified areas for improving the algorithm, including expanding the algorithm's vocabulary for the highest quality processing of word forms. In addition, authors noted about alternative technologies that might be used for enhancing current algorithm.
KW - NLP
KW - Sentiment analysis
KW - Uzbek language
KW - idioms
KW - lexicon-based algorithm
KW - low-resource languages
KW - morphological analysis
KW - natural language processing
KW - negations
KW - text emotional tone
KW - weighted lexical features
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85201958147&origin=inward&txGid=5a21af0fa0dde7170784ef006d0141ca
UR - https://www.mendeley.com/catalogue/f57ef073-4558-3166-85df-ef0ffa2af53f/
U2 - 10.1109/EDM61683.2024.10615124
DO - 10.1109/EDM61683.2024.10615124
M3 - Conference contribution
SN - 9798350389234
T3 - International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM
SP - 2450
EP - 2453
BT - International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM
PB - IEEE Computer Society
T2 - 25th IEEE International Conference of Young Professionals in Electron Devices and Materials
Y2 - 28 June 2024 through 2 July 2024
ER -
ID: 60549143