SENTIMENT TAHLILI UCHUN LINGVISTIK TA’MINOTNI ANNOTATSIYALASH SXEMASI VA KO‘RSATMALARI
Jild 4 son 28 (2025): Innovative research in the modern world: theory and practice 107-111
Annotatsiya
Ushbu maqolada sentiment tahlili uchun lingvistik ta’minotni ishlab chiqishda annotatsiyalash sxemasi va ko‘rsatmalarni yaratish masalalari tahlil qilinadi. Taklif etilgan sxema matnlarning baholovchi xususiyatlarini aniqlash, ya’ni ijobiy, salbiy va neytral munosabatlarni belgilash imkonini beradi. Shuningdek, emotsional-ekspressiv birliklarning identifikatsiyasi ham ko‘zda tutiladi. Annotatsiyalash jarayonini standartlashtirish uchun ishlab chiqilgan ko‘rsatmalar annotatorlarning bir xil yondashuvni qo‘llashiga yordam beradi va izchillikni ta’minlaydi. Tadqiqot natijalari o‘zbek tili matnlarini sentiment tahlil qilishda samarali lingvistik resurslarni yaratishda qo‘llanishi mumkin.
Kalit soʻzlar:
Iqtiboslar
Du, J., Wang, D., Lin, B., He, L., Huang, L. C., Wang, J., Manion, F. J., Li, Y., Cossrow, N., & Yao, L. (2025). Use of deep learning-based NLP models for full-text data elements extraction for systematic literature review tasks. Scientific reports, 15(1), 19379. https://doi.org/10.1038/s41598-025-03979-5
Amusat, O. O., Hegde, H., Mungall, C. J., Giannakou, A., Byers, N. P., Gunter, D., Fagnan, K., & Ramakrishnan, L. (2024). Automated annotation of scientific texts for ML-based keyphrase extraction and validation. Database : the journal of biological databases and curation, 2024, baae093. https://doi.org/10.1093/database/baae093
Mozetič, I., Grčar, M., & Smailović, J. (2016). Multilingual Twitter Sentiment Classification: The Role of Human Annotators. PloS one, 11(5), e0155036. https://doi.org/10.1371/journal.pone.0155036
Samreen, A., & Ali, S. A. (2023). Dataset construction to detect human behavior with the help of emotions, sentiments and mood for Roman Urdu. Data in brief, 52, 109906. https://doi.org/10.1016/j.dib.2023.109906
Yang, F., Zamzmi, G., Angara, S., Rajaraman, S., Aquilina, A., Xue, Z., Jaeger, S., Papagiannakis, E., & Antani, S. K. (2023). Assessing Inter-Annotator Agreement for Medical Image Segmentation. IEEE access : practical innovations, open solutions, 11, 21300–21312. https://doi.org/10.1109/access.2023.3249759
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