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 -