• Academic Research in Educational Sciences Volume 3 | Issue 4 | 2022 ISSN: 2181-1385 Cite-Factor: 0,89 | SIS: 1,12
  • Academic Research in Educational Sciences Volume 3




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    XULOSA 
    Ma‟lumki, biror mahsulot yoki muammoga qiziqish dastlab uni o„rganishdan 
    boshlanadi. Ijtimoiy tarmoqlarda esa istalgancha izlash imkoni mavjud. Axborotlar 
    shunchalik koʻpki, bu maʼlumotlarni toʻliq oʻrganib chiqish uchun bir kishining 24 
    soat vaqti kifoya qilmaydi. Sentiment tahlilining maqsadi ijtimoy tarmoqlarning 
    kuchidan turli xil sohalardagi kayfiyatni o„rganishdan iborat. 
     
    REFERENCES

    1. Abduraxmonova N. Z. "Linguistic support of the program for translating English 


    texts into Uzbek (on the example of simple sentences): Doctor of Philosophy (PhD) il 
    dis. aftoref." (2018). 
    2. Abdurakhmonova N. The bases of automatic morphological analysis for machine 
    translation. Izvestiya Kyrgyzskogo gosudarstvennogo tekhnicheskogo universiteta. 
    2016;2 (38):12-7. 
    3. Abdurakhmonova N, Tuliyev U. Morphological analysis by finite state transducer 
    for Uzbek-English machine translation/Foreign Philology: Language. Literature
    Education. 2018(3):68. 
    4. Abdurakhmonova N, Urdishev K. Corpus based teaching Uzbek as a foreign 
    language. Journal of Foreign Language Teaching and Applied Linguistics (J-
    FLTAL). 2019;6(1-2019):131-7. 
    5. Abdurakhmonov N. Modeling Analytic Forms of Verb in 
    Uzbek as Stage of Morphological Analysis in Machine 


    Academic Research in Educational Sciences 
    Volume 3 | Issue 4 | 2022
    ISSN: 2181-1385 
    Cite-Factor: 0,89 | SIS: 1,12
    DOI: 10.24412/2181-1385-2022-4-358-364 
    SJIF: 5,7 | UIF: 6,1 
     
     
     
     
    364
     
    April, 2022 
    https://t.me/ares_uz Multidisciplinary Scientific Journal 
    Translation. Journal of Social Sciences and Humanities Research. 2017;5(03):89-100. 
    6. Abdurakhmonova N. Dependency parsing based on Uzbek Corpus. InProceedings 
    of the International Conference on Language Technologies for All (LT4All) 2019. 
    7. Aripov M., Sharipbay A., Abdurakhmonova N., Razakhova B.: Ontology of 
    grammar rules as example of noun of Uzbek and Kazakh languages. In: Abstract of 
    the VI International Conference “Modern Problems of Applied Mathematics and 
    Information Technology - Al-Khorezmiy 2018”, pp. 37–38, Tashkent, Uzbekistan 
    (2018) 
    8. Go A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using 
    distant supervision. CS224N Project Report, Stanford, 1(12). 
    9. Kubedinova L. Khusainov A., Suleymanov D., Gilmullin R., Abdurakhmonova N. 
    First Results of the TurkLang-7 Project: Creating Russian-Turkic Parallel Corpora 
    and MT Systems. Proceedings of the Computational Models in Language and Speech 
    Workshop (CMLS 2020) co-located with 16th International Conference on 
    Computational and Cognitive Linguistics (TEL 2020) .2020/11: 90-101 
    10. Kuriyozov E., Matlatipov S. Building a new Sentiment Analysis Dataset for 
    Uzbek Language and Creating Baseline Models.// Multidisciplinary Digital 
    Publishing Institute Proceedings. 2019.-№1. Pages 37. 
    11. Rabbimov I., Mporas I., Kobilov S. Investigating the effect of emoji in opinion 
    classification of uzbek movie rewiev comments. International Conference on Speech 
    and Computer Science. -2020.–P.435-445. 
    12. Chen Yanqing and Steven Skiena, 2014. Building sentiment lexicons for all 
    major languages. In Proceedings of the 52nd Annual Meeting of the Association for 
    Computational Linguistics (Vol. 2: Short Papers). Baltimore, Maryland: Association 
    for Computational Linguistics. Chollet, Franc¸ois et al., 2015. Keras. https:// 
    github.com/fchollet/keras. 

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