Standard

Entity-Aware Sentiment Analysis for Uzbek Social Media Using XLM-RoBERTa NER and LSTM. / Saidov, Bobur R.; Fayzullaeva, Zarnigor; Tursunov, Ulugbek A. и др.

Proceedings - 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2026. Institute of Electrical and Electronics Engineers Inc., 2026. (Proceedings - 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2026).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › глава/раздел › научная › Рецензирование

Harvard

Saidov, BR, Fayzullaeva, Z, Tursunov, UA, Narmuratov, Z, Egamova, SD & Gulbakhar, BA 2026, Entity-Aware Sentiment Analysis for Uzbek Social Media Using XLM-RoBERTa NER and LSTM. в Proceedings - 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2026. Proceedings - 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2026, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/USBEREIT70063.2026.11580443

APA

Saidov, B. R., Fayzullaeva, Z., Tursunov, U. A., Narmuratov, Z., Egamova, S. D., & Gulbakhar, B. A. (2026). Entity-Aware Sentiment Analysis for Uzbek Social Media Using XLM-RoBERTa NER and LSTM. в Proceedings - 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2026 (Proceedings - 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2026). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/USBEREIT70063.2026.11580443

Vancouver

Saidov BR, Fayzullaeva Z, Tursunov UA, Narmuratov Z, Egamova SD, Gulbakhar BA. Entity-Aware Sentiment Analysis for Uzbek Social Media Using XLM-RoBERTa NER and LSTM. в Proceedings - 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2026. Institute of Electrical and Electronics Engineers Inc. 2026. (Proceedings - 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2026). doi: 10.1109/USBEREIT70063.2026.11580443

Author

Saidov, Bobur R. ; Fayzullaeva, Zarnigor ; Tursunov, Ulugbek A. и др. / Entity-Aware Sentiment Analysis for Uzbek Social Media Using XLM-RoBERTa NER and LSTM. Proceedings - 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2026. Institute of Electrical and Electronics Engineers Inc., 2026. (Proceedings - 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2026).

BibTeX

@inbook{c6fb4a26dd9e4609a35e2d8805c2fa14,
title = "Entity-Aware Sentiment Analysis for Uzbek Social Media Using XLM-RoBERTa NER and LSTM",
abstract = "This paper studies how named entities influence sentiment polarity in Uzbek social media and proposes an entity-aware sentiment analysis pipeline. We collected 10,000 Uzbek posts from Telegram, Instagram, and Facebook (January-June 2025) and annotated three entity types (Person, Brand, Geolocation) in a subset of 4,600 posts. Named entities are extracted with an adapted XLM-RoBERTa NER model, while sentiment is predicted by an LSTM classifier with Uzbek Word2Vec embeddings. To account for the different impact of entity types, we introduce an entity-weighted adjustment that calibrates sentiment scores using empirically estimated coefficients. On the held-out test split, the proposed approach achieves strong sentiment performance, reaching Test Accuracy=0.84 and Macro-F1=0.83 (Macro Precision=0.83, Macro Recall = 0.82). Statistical validation confirms significant differences across entity categories (ANOVA) and a positive association between entity mentions and model confidence (Pearson r=0.82). The approach supports practical analytics scenarios such as brand reputation monitoring, public communication tracking, and tourism feedback analysis for Uzbek-language data.",
keywords = "NLP, Uzbek language, named objects, sentiment analysis, social networks, Моделирование, Управление брендом, Печать, Социальные сети (онлайн), Тестирование, Анализ настроений, Дисперсионный анализ, Разметка, Многоязычность, Долговременная кратковременная память, именованные объекты, анализ настроений, узбекский язык, НЛП, социальные сети",
author = "Saidov, {Bobur R.} and Zarnigor Fayzullaeva and Tursunov, {Ulugbek A.} and Zayniddin Narmuratov and Egamova, {Shokhida D.} and Gulbakhar, {Begmuratova A.}",
note = "B. R. Saidov, Z. Fayzullaeva, U. A. Tursunov, Z. Narmuratov, S. D. Egamova and B. A. Gulbakhar, {"}Entity-Aware Sentiment Analysis for Uzbek Social Media Using XLM-RoBERTa NER and LSTM,{"} 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), Yekaterinburg, Russian Federation, 2026, pp. 1-4, doi: 10.1109/USBEREIT70063.2026.11580443. keywords: {Modeling;Brand management;Printing;Social networking (online);Testing;Sentiment analysis;Analysis of variance;Labeling;Multilingual;Long short term memory;named objects;sentiment analysis;Uzbek language;NLP;social networks}, ",
year = "2026",
doi = "10.1109/USBEREIT70063.2026.11580443",
language = "English",
isbn = "9798319541772",
series = "Proceedings - 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2026",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2026",
address = "United States",

}

RIS

TY - CHAP

T1 - Entity-Aware Sentiment Analysis for Uzbek Social Media Using XLM-RoBERTa NER and LSTM

AU - Saidov, Bobur R.

AU - Fayzullaeva, Zarnigor

AU - Tursunov, Ulugbek A.

AU - Narmuratov, Zayniddin

AU - Egamova, Shokhida D.

AU - Gulbakhar, Begmuratova A.

N1 - B. R. Saidov, Z. Fayzullaeva, U. A. Tursunov, Z. Narmuratov, S. D. Egamova and B. A. Gulbakhar, "Entity-Aware Sentiment Analysis for Uzbek Social Media Using XLM-RoBERTa NER and LSTM," 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), Yekaterinburg, Russian Federation, 2026, pp. 1-4, doi: 10.1109/USBEREIT70063.2026.11580443. keywords: {Modeling;Brand management;Printing;Social networking (online);Testing;Sentiment analysis;Analysis of variance;Labeling;Multilingual;Long short term memory;named objects;sentiment analysis;Uzbek language;NLP;social networks},

PY - 2026

Y1 - 2026

N2 - This paper studies how named entities influence sentiment polarity in Uzbek social media and proposes an entity-aware sentiment analysis pipeline. We collected 10,000 Uzbek posts from Telegram, Instagram, and Facebook (January-June 2025) and annotated three entity types (Person, Brand, Geolocation) in a subset of 4,600 posts. Named entities are extracted with an adapted XLM-RoBERTa NER model, while sentiment is predicted by an LSTM classifier with Uzbek Word2Vec embeddings. To account for the different impact of entity types, we introduce an entity-weighted adjustment that calibrates sentiment scores using empirically estimated coefficients. On the held-out test split, the proposed approach achieves strong sentiment performance, reaching Test Accuracy=0.84 and Macro-F1=0.83 (Macro Precision=0.83, Macro Recall = 0.82). Statistical validation confirms significant differences across entity categories (ANOVA) and a positive association between entity mentions and model confidence (Pearson r=0.82). The approach supports practical analytics scenarios such as brand reputation monitoring, public communication tracking, and tourism feedback analysis for Uzbek-language data.

AB - This paper studies how named entities influence sentiment polarity in Uzbek social media and proposes an entity-aware sentiment analysis pipeline. We collected 10,000 Uzbek posts from Telegram, Instagram, and Facebook (January-June 2025) and annotated three entity types (Person, Brand, Geolocation) in a subset of 4,600 posts. Named entities are extracted with an adapted XLM-RoBERTa NER model, while sentiment is predicted by an LSTM classifier with Uzbek Word2Vec embeddings. To account for the different impact of entity types, we introduce an entity-weighted adjustment that calibrates sentiment scores using empirically estimated coefficients. On the held-out test split, the proposed approach achieves strong sentiment performance, reaching Test Accuracy=0.84 and Macro-F1=0.83 (Macro Precision=0.83, Macro Recall = 0.82). Statistical validation confirms significant differences across entity categories (ANOVA) and a positive association between entity mentions and model confidence (Pearson r=0.82). The approach supports practical analytics scenarios such as brand reputation monitoring, public communication tracking, and tourism feedback analysis for Uzbek-language data.

KW - NLP

KW - Uzbek language

KW - named objects

KW - sentiment analysis

KW - social networks

KW - Моделирование

KW - Управление брендом

KW - Печать

KW - Социальные сети (онлайн)

KW - Тестирование

KW - Анализ настроений

KW - Дисперсионный анализ

KW - Разметка

KW - Многоязычность

KW - Долговременная кратковременная память

KW - именованные объекты

KW - анализ настроений

KW - узбекский язык

KW - НЛП

KW - социальные сети

UR - https://www.mendeley.com/catalogue/71f507e2-0446-3c5e-833d-945c63a98699/

UR - https://www.scopus.com/pages/publications/105044705736

U2 - 10.1109/USBEREIT70063.2026.11580443

DO - 10.1109/USBEREIT70063.2026.11580443

M3 - Chapter

SN - 9798319541772

T3 - Proceedings - 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2026

BT - Proceedings - 2026 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2026

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

ID: 83282028