Sentiment analysis and determination of aspects with ratings in social comments through trained generative models

Rajabov , Jaloliddin, Matlatipov, Sanatbek

Techscience.uz - техника фанлари долзарб масалалри · 2025-yil

Annotatsiya

Typically, a review includes not only an overall rating but also ratings for several aspects and accompanying text. The rating is considered a numerical representation of the author's overall satisfaction. Although the number of reviews with aspect-specific ratings is increasing, there are still many reviews that only provide an overall rating. Extracting hidden aspect-related opinions from such reviews helps users quickly understand the gist without reading the entire text. This task mainly consists of two parts: identifying aspects and assigning ratings. Most existing studies cannot utilize the aspect ratings that have been increasingly available in recent years. In this article, we examine two artificial intelligence models that improve the efficiency of assigning aspect ratings to unseen reviews. Specifically, we look at sentiment words and aspect-level sentiment distributions that generate aspect ratings

Maqola ma’lumotlari
MualliflarRajabov , Jaloliddin, Matlatipov, Sanatbek
JurnalTechscience.uz - техника фанлари долзарб масалалри
NashriyotЖиззах политехника институти
ISSN3030-3702, 3030-3702
Nashr sanasi2025-08-11
Jild3
Son5
Betlar41-50
TilIngliz
Ilmiy sohaTexnika
DOI10.47390/ts-v3i5y2025n7
Indeksga qo‘shilgan2026-08-26

DOI: 10.47390/ts-v3i5y2025n7 · Maqolaning asl sahifasi

Kalit so‘zlar

Uzbek language, sentiment analysis, text processing, aspect identification, O‘zbek tili, sentiment tahlil, matnlarni qayta ishlash, aspektlarni aniqlash

Ilmiy soha

Texnika

Iqtibos keltirish

APARajabov , Jaloliddin, & Matlatipov, Sanatbek (2025). SENTIMENT ANALYSIS AND DETERMINATION OF ASPECTS WITH RATINGS IN SOCIAL COMMENTS THROUGH TRAINED GENERATIVE MODELS. Techscience.uz - техника фанлари долзарб масалалри, 3(5), 41-50. https://doi.org/10.47390/ts-v3i5y2025n7
GOST (OAK)Rajabov , Jaloliddin, Matlatipov, Sanatbek SENTIMENT ANALYSIS AND DETERMINATION OF ASPECTS WITH RATINGS IN SOCIAL COMMENTS THROUGH TRAINED GENERATIVE MODELS // Techscience.uz - техника фанлари долзарб масалалри. – 2025. – T. 3, № 5. – P. 41-50. – DOI: 10.47390/ts-v3i5y2025n7.
MLARajabov , Jaloliddin, and Matlatipov, Sanatbek. “SENTIMENT ANALYSIS AND DETERMINATION OF ASPECTS WITH RATINGS IN SOCIAL COMMENTS THROUGH TRAINED GENERATIVE MODELS.” Techscience.uz - техника фанлари долзарб масалалри, 3(5), 41-50, 2025. https://doi.org/10.47390/ts-v3i5y2025n7
ChicagoRajabov , Jaloliddin, and Matlatipov, Sanatbek. “SENTIMENT ANALYSIS AND DETERMINATION OF ASPECTS WITH RATINGS IN SOCIAL COMMENTS THROUGH TRAINED GENERATIVE MODELS.” Techscience.uz - техника фанлари долзарб масалалри, 3(5), 41-50 (2025). https://doi.org/10.47390/ts-v3i5y2025n7
HarvardRajabov , Jaloliddin, and Matlatipov, Sanatbek (2025) ‘SENTIMENT ANALYSIS AND DETERMINATION OF ASPECTS WITH RATINGS IN SOCIAL COMMENTS THROUGH TRAINED GENERATIVE MODELS’, Techscience.uz - техника фанлари долзарб масалалри, 3(5), 41-50. https://doi.org/10.47390/ts-v3i5y2025n7
BibTeX
@article{rajabovjaloliddin2025,
  title = {SENTIMENT ANALYSIS AND DETERMINATION OF ASPECTS WITH RATINGS IN SOCIAL COMMENTS THROUGH TRAINED GENERATIVE MODELS},
  author = {Rajabov , Jaloliddin and Matlatipov, Sanatbek},
  journal = {Techscience.uz - техника фанлари долзарб масалалри},
  year = {2025},
  volume = {3},
  number = {5},
  pages = {41-50},
  doi = {10.47390/ts-v3i5y2025n7},
  url = {https://doi.org/10.47390/ts-v3i5y2025n7}
}

Boshqa maqola uchun iqtibos tayyorlash

Techscience.uz - техника фанлари долзарб масалалри jurnalidan boshqa maqolalar

Techscience.uz - техника фанлари долзарб масалалри — barcha maqolalar · 2025-yil arxivi

Rajabov , Jaloliddin — boshqa maqolalari

Texnika sohasidagi boshqa maqolalar

Texnika sohasidagi OAK jurnallari