{"id":13400,"date":"2022-08-21T11:07:07","date_gmt":"2022-08-21T08:07:07","guid":{"rendered":"http:\/\/journals.khnu.km.ua\/vestnik\/?p=13400"},"modified":"2022-10-03T15:05:15","modified_gmt":"2022-10-03T12:05:15","slug":"algorytm-vydobuvannya-ta-opraczyuvannya-sporidnenyh-danyh-v-soczialnyh-merezhah","status":"publish","type":"post","link":"https:\/\/journals.khnu.km.ua\/vestnik\/?p=13400","title":{"rendered":"\u0410\u043b\u0433\u043e\u0440\u0438\u0442\u043c \u0432\u0438\u0434\u043e\u0431\u0443\u0432\u0430\u043d\u043d\u044f \u0442\u0430 \u043e\u043f\u0440\u0430\u0446\u044e\u0432\u0430\u043d\u043d\u044f \u0441\u043f\u043e\u0440\u0456\u0434\u043d\u0435\u043d\u0438\u0445 \u0434\u0430\u043d\u0438\u0445 \u0432 \u0441\u043e\u0446\u0456\u0430\u043b\u044c\u043d\u0438\u0445 \u043c\u0435\u0440\u0435\u0436\u0430\u0445"},"content":{"rendered":"

<\/p>\n

\u0410\u041b\u0413\u041e\u0420\u0418\u0422\u041c \u0412\u0418\u0414\u041e\u0411\u0423\u0412\u0410\u041d\u041d\u042f \u0422\u0410 \u041e\u041f\u0420\u0410\u0426\u042e\u0412\u0410\u041d\u041d\u042f \u0421\u041f\u041e\u0420\u0406\u0414\u041d\u0415\u041d\u0418\u0425 \u0414\u0410\u041d\u0418\u0425 \u0412 \u0421\u041e\u0426\u0406\u0410\u041b\u042c\u041d\u0418\u0425 \u041c\u0415\u0420\u0415\u0416\u0410\u0425<\/p>\n

ALGORITHM OF DATA MINING AND PROCESSING \u00a0OF RELATED DATA IN SOCIAL NETWORKS<\/p>\n

\u0421\u0442\u043e\u0440\u0456\u043d\u043a\u0438 <\/strong>115<\/strong>–<\/strong>118<\/strong>. \u041d\u043e\u043c\u0435\u0440: \u21164, 2022 (311)\u00a0\u00a0 \"\"<\/a> <\/strong>
\n\u0410\u0432\u0442\u043e\u0440\u0438:<\/strong>
\n\u041a\u0420\u0418\u0412\u0415\u041d\u0427\u0423\u041a \u042e. \u041f.
\n\u041d\u0430\u0446\u0456\u043e\u043d\u0430\u043b\u044c\u043d\u0438\u0439 \u0443\u043d\u0456\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442 \u201c\u041b\u044c\u0432\u0456\u0432\u0441\u044c\u043a\u0430 \u043f\u043e\u043b\u0456\u0442\u0435\u0445\u043d\u0456\u043a\u0430\u201d
\nhttps:\/\/orcid.org\/0000-0002-2504-5833<\/a>
\ne-mail: Yurii.P.Kryvenchuk@lpnu.ua<\/a>
\n\u0425\u0410\u041d\u0410\u0421 \u041c.-\u042e. \u0420.
\n\u041d\u0430\u0446\u0456\u043e\u043d\u0430\u043b\u044c\u043d\u0438\u0439 \u0443\u043d\u0456\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442 \u201c\u041b\u044c\u0432\u0456\u0432\u0441\u044c\u043a\u0430 \u043f\u043e\u043b\u0456\u0442\u0435\u0445\u043d\u0456\u043a\u0430\u201d
\ne-mail: hanasura79@gmail.com<\/a>
\nYurii KRYVENCHUK, Mykhailo-Yurii KHANAS
\nLviv Polytechnic National University
\nDOI:<\/strong>\u00a0https:\/\/www.doi.org\/10.31891\/2307-5732-2022-311-4-115-118<\/a><\/p>\n

\u0410\u043d\u043e\u0442\u0430\u0446\u0456\u044f \u043c\u043e\u0432\u043e\u044e \u043e\u0440\u0438\u0433\u0456\u043d\u0430\u043b\u0443<\/strong><\/p>\n

\u041e\u0441\u043d\u043e\u0432\u043e\u044e \u0434\u0430\u043d\u043e\u0457 \u0440\u043e\u0431\u043e\u0442\u0438 \u0454 \u043e\u0434\u0438\u043d \u0437 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0456\u0432, \u0449\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0442\u044c\u0441\u044f \u0432 \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0430\u0446\u0456\u0439\u043d\u0438\u0445 \u0441\u0438\u0441\u0442\u0435\u043c\u0430\u0445 – \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0430\u0446\u0456\u0439\u043d\u0430 \u0441\u0438\u0441\u0442\u0435\u043c\u0430 \u043e\u0441\u043d\u043e\u0432\u0430\u043d\u0430 \u043d\u0430 \u0444\u0456\u043b\u044c\u0442\u0440\u0430\u0446\u0456\u0457 \u0432\u043c\u0456\u0441\u0442\u0443. \u0412\u043e\u043d\u0430 \u0430\u043d\u0430\u043b\u0456\u0437\u0443\u0454 \u043f\u043e\u0441\u0442\u0438 \u043a\u043e\u0440\u0438\u0441\u0442\u0443\u0432\u0430\u0447\u0456\u0432 \u0443 Twitter \u0442\u0430 \u0432\u0438\u0440\u0430\u0445\u043e\u0432\u0443\u0454 \u0457\u0445 \u0456\u043d\u0442\u0435\u0440\u0435\u0441\u0438. \u041e\u0441\u043e\u0431\u043b\u0438\u0432\u0456\u0441\u0442\u044e \u0434\u0430\u043d\u043e\u0457 \u0441\u0438\u0441\u0442\u0435\u043c\u0438 \u0454 \u0442\u0435, \u0449\u043e \u0434\u0430\u043d\u0438\u0439 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454 \u043f\u0430\u0440\u0430\u043b\u0435\u043b\u044c\u043d\u0456 \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044f \u0442\u0430 \u0447\u0430\u0441\u0442\u043e\u0442\u043d\u0438\u0439 \u0430\u043d\u0430\u043b\u0456\u0437 \u0442\u0435\u043a\u0441\u0442\u0443. \u0426\u0435 \u0434\u0430\u0454 \u0437\u043c\u043e\u0433\u0443 \u043e\u0431\u2019\u0454\u0434\u043d\u0430\u0442\u0438 \u043b\u044e\u0434\u0435\u0439 \u0437 \u043e\u0434\u043d\u0430\u043a\u043e\u0432\u0438\u043c\u0438 \u0456\u043d\u0442\u0435\u0440\u0435\u0441\u0430\u043c\u0438.
\n\u041a\u043b\u044e\u0447\u043e\u0432\u0456 \u0441\u043b\u043e\u0432\u0430:<\/strong> \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0430\u0446\u0456\u0439\u043d\u0430 \u0441\u0438\u0441\u0442\u0435\u043c\u0430, \u0434\u0430\u0442\u0430 \u043c\u0430\u0439\u043d\u0456\u043d\u0433, big data, nltk, tweepy.<\/p>\n

\u0420\u043e\u0437\u0448\u0438\u0440\u0435\u043d\u0430 \u0430\u043d\u043e\u0442\u0430\u0446\u0456\u044f \u0430\u043d\u0433\u043b\u0456\u0439\u0441\u044c\u043a\u043e\u044e \u00a0\u043c\u043e\u0432\u043e\u044e<\/strong><\/p>\n

We live in a time of rapid growth of information technology, which is firmly entrenched in our daily lives. It is simply impossible to imagine a modern person without social networks, because they perform a communicative and informational function, namely: communication, information retrieval, news exchange, etc. Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. So, there is a lot of information available to the user, which is difficult to identify something specific and necessary in the usual way viewing. Accordingly, there is a need for technologies that can quickly process large amounts of data and highlight only the information that is useful to a particular user. This technology called recommender systems. It automatically suggest items to users that might be interesting for them. Due to the desire to unite people with common interests, it is relevant to develop a recommendation system based on social networks that help in personification of the user and compilation of his psychotype using his profile.
\nThe paper has description and results of the creation of recommendation system. The basis of this work is one of the algorithms used in recommendation systems – the recommendation system is based on content filtering. It analyzes users’ Twitter posts and calculates their interests. If we consider all the words, our model will not have good results and do not pay attention to what is important to use. Therefore, the most important step is always filtering data, so the number one task is to speed up the time of filtering text and retrieving data from the social network for further processing. The feature of this system is that this algorithm uses parallel calculations and frequency analysis of the text.
\nKeywords:<\/strong> recommendation system, date mining, big data, nltk, tweepy.<\/p>\n

\u041b\u0456\u0442\u0435\u0440\u0430\u0442\u0443\u0440\u0430<\/strong><\/p>\n

    \n
  1. He, Z. He, X. Du, and T.-S. Chua, \u201cAdversarial personalized ranking for recommendation,\u201d in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 2018<\/li>\n
  2. Tang, X. Hu, and H. Liu, \u201cSocial recommendation: a review,\u201d Social Network Analysis and Mining, vol. 3, no. 4, 2013.<\/li>\n
  3. C. Valverde-Rebaza, M. Roche, P. Poncelet, and A. D. A. Lopes, The role of location and social strength for friendship prediction in location-based social networks, Information Processing and Management, vol. 54, no. 4, 2018, doi: 10.1016\/j.ipm.2018.02.004.<\/li>\n
  4. Zhao, Q. Yao, J. T. Kwok, and D. L. Lee, Collaborative filtering with social local models, 2017, vol. 2017-November, pp. 645\u2013654. doi: 10.1109\/ICDM.2017.74.<\/li>\n
  5. Liu, P. Zhao, X. Liu, M. Wu, and X.-L. Li. Learning Optimal Social Dependency for Recommendation. arXiv preprint arXiv:1603.04522, 2016.<\/li>\n
  6. Steven Bird, Ewan Klein, and Edward Loper, Natural Language Processing with Python, Analyzing Text with the Natural Language Toolkit, O’Reilly Media, 2009<\/li>\n
  7. Yu, M. Gao, J. Li, H. Yin, and H. Liu, \u201cAdaptive implicit friends identification over heterogeneous network for social recommendation,\u201d in Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 2018<\/li>\n
  8. Davoudi and M. Chatterjee. Modeling trust for rating prediction in recommender systems. In SIAM Workshop on Machine Learning Methods for Recommender Systems, SIAM, 2016.<\/li>\n<\/ol>\n

    References<\/strong><\/p>\n

      \n
    1. He, Z. He, X. Du, and T.-S. Chua, \u201cAdversarial personalized ranking for recommendation,\u201d in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 2018<\/li>\n
    2. Tang, X. Hu, and H. Liu, \u201cSocial recommendation: a review,\u201d Social Network Analysis and Mining, vol. 3, no. 4, 2013.<\/li>\n
    3. C. Valverde-Rebaza, M. Roche, P. Poncelet, and A. D. A. Lopes, The role of location and social strength for friendship prediction in location-based social networks, Information Processing and Management, vol. 54, no. 4, 2018, doi: 10.1016\/j.ipm.2018.02.004.<\/li>\n
    4. Zhao, Q. Yao, J. T. Kwok, and D. L. Lee, Collaborative filtering with social local models, 2017, vol. 2017-November, pp. 645\u2013654. doi: 10.1109\/ICDM.2017.74.<\/li>\n
    5. Liu, P. Zhao, X. Liu, M. Wu, and X.-L. Li. Learning Optimal Social Dependency for Recommendation. arXiv preprint arXiv:1603.04522, 2016.<\/li>\n
    6. Steven Bird, Ewan Klein, and Edward Loper, Natural Language Processing with Python, Analyzing Text with the Natural Language Toolkit, O’Reilly Media, 2009<\/li>\n
    7. Yu, M. Gao, J. Li, H. Yin, and H. Liu, \u201cAdaptive implicit friends identification over heterogeneous network for social recommendation,\u201d in Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 2018<\/li>\n
    8. Davoudi and M. Chatterjee. Modeling trust for rating prediction in recommender systems. In SIAM Workshop on Machine Learning Methods for Recommender Systems, SIAM, 2016.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[67],"tags":[],"_links":{"self":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/13400"}],"collection":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=13400"}],"version-history":[{"count":4,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/13400\/revisions"}],"predecessor-version":[{"id":14145,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=\/wp\/v2\/posts\/13400\/revisions\/14145"}],"wp:attachment":[{"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=13400"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=13400"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/journals.khnu.km.ua\/vestnik\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=13400"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}