{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T13:15:00Z","timestamp":1743081300868,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030835262"},{"type":"electronic","value":"9783030835279"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-83527-9_16","type":"book-chapter","created":{"date-parts":[[2021,8,29]],"date-time":"2021-08-29T23:04:59Z","timestamp":1630278299000},"page":"184-196","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Use of Augmentation and Distant Supervision for Sentiment Analysis in Russian"],"prefix":"10.1007","author":[{"given":"Anton","family":"Golubev","sequence":"first","affiliation":[]},{"given":"Natalia","family":"Loukachevitch","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,30]]},"reference":[{"key":"16_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/978-3-642-40802-1_31","volume-title":"Information Access Evaluation. Multilinguality, Multimodality, and Visualization","author":"E Amig\u00f3","year":"2013","unstructured":"Amig\u00f3, E., et al.: Overview of RepLab 2013: evaluating online reputation monitoring systems. In: Forner, P., M\u00fcller, H., Paredes, R., Rosso, P., Stein, B. (eds.) CLEF 2013. LNCS, vol. 8138, pp. 333\u2013352. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40802-1_31"},{"key":"16_CR2","unstructured":"Baymurzina, D., Kuznetsov, D., Burtsev, M.: Language model embeddings improve sentiment analysis in Russian. In: Komp\u2019juternaja Lingvistika i Intellektual\u2019nye Tehnologii, pp. 53\u201362 (2019)"},{"key":"16_CR3","volume-title":"Natural Language Processing with Python","author":"S Bird","year":"2009","unstructured":"Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O\u2019reilly Media Inc., Sebastopol (2009)"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Burtsev, M.: DeepPavlov: open-source library for dialogue systems. In: Proceedings of ACL 2018, System Demonstrations, pp. 122\u2013127 (2018)","DOI":"10.18653\/v1\/P18-4021"},{"key":"16_CR5","unstructured":"Chetviorkin, I., Loukachevitch, N.: Evaluating sentiment analysis systems in Russian. In: Proceedings of the 4th Biennial International Workshop on Balto-Slavic Natural Language Processing, pp. 12\u201317 (2013)"},{"key":"16_CR6","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"issue":"1","key":"16_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40649-020-00080-x","volume":"8","author":"H-T Duong","year":"2020","unstructured":"Duong, H.-T., Nguyen-Thi, T.-A.: A review: preprocessing techniques and data augmentation for sentiment analysis. Comput. Soc. Netw. 8(1), 1\u201316 (2020). https:\/\/doi.org\/10.1186\/s40649-020-00080-x","journal-title":"Comput. Soc. Netw."},{"key":"16_CR8","unstructured":"Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N project report, Stanford 1(12), 2009 (2009)"},{"key":"16_CR9","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/978-3-030-59082-6_8","volume-title":"Artificial Intelligence and Natural Language","author":"A Golubev","year":"2020","unstructured":"Golubev, A., Loukachevitch, N.: Improving results on Russian sentiment datasets. In: Filchenkov, A., Kauttonen, J., Pivovarova, L. (eds.) AINL 2020. CCIS, vol. 1292, pp. 109\u2013121. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59082-6_8"},{"key":"16_CR10","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.1212303","author":"M Honnibal","year":"2020","unstructured":"Honnibal, M., Montani, I., Van Landeghem, S., Boyd, A.: SpaCy: industrial-strength natural language processing in python. Zenodo (2020). https:\/\/doi.org\/10.5281\/zenodo.1212303","journal-title":"Zenodo"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Kobayashi, S.: Contextual augmentation: data augmentation by words with paradigmatic relations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Short Papers), vol. 2, pp. 452\u2013457 (2018)","DOI":"10.18653\/v1\/N18-2072"},{"key":"16_CR12","unstructured":"Loukachevitch, N., Dobrov, B.V.: RuTHes linguistic ontology vs. Russian wordnets. In: Proceedings of the Seventh Global Wordnet Conference, pp. 154\u2013162 (2014)"},{"key":"16_CR13","unstructured":"Loukachevitch, N., Levchik, A.: Creating a general Russian sentiment lexicon. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), pp. 1171\u20131176 (2016)"},{"key":"16_CR14","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1007\/978-3-319-24033-6_62","volume-title":"Text, Speech, and Dialogue","author":"N Loukachevitch","year":"2015","unstructured":"Loukachevitch, N., Rubtsova, Y.: Entity-oriented sentiment analysis of tweets: results and problems. In: Kr\u00e1l, P., Matou\u0161ek, V. (eds.) TSD 2015. LNCS (LNAI), vol. 9302, pp. 551\u2013559. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24033-6_62"},{"key":"16_CR15","unstructured":"Loukachevitch, N., Rubtsova, Y.: SentiRuEval-2016: overcoming time gap and data sparsity in tweet sentiment analysis. In: Proceedings of International Conference Dialog-2016 (2016)"},{"issue":"11","key":"16_CR16","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller, G.A.: Wordnet: a lexical database for English. Commun. ACM 38(11), 39\u201341 (1995)","journal-title":"Commun. ACM"},{"key":"16_CR17","unstructured":"Mohammad, S., Salameh, M., Kiritchenko, S.: Sentiment lexicons for Arabic social media. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), pp. 33\u201337 (2016)"},{"key":"16_CR18","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1007\/978-3-030-59535-7_20","volume-title":"Artificial Intelligence","author":"V Moshkin","year":"2020","unstructured":"Moshkin, V., Konstantinov, A., Yarushkina, N.: Application of the BERT language model for sentiment analysis of social network posts. In: Kuznetsov, S.O., Panov, A.I., Yakovlev, K.S. (eds.) RCAI 2020. LNCS (LNAI), vol. 12412, pp. 274\u2013283. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59535-7_20"},{"key":"16_CR19","unstructured":"Rogers, A., Romanov, A., Rumshisky, A., Volkova, S., Gronas, M., Gribov, A.: RuSentiment: an enriched sentiment analysis dataset for social media in Russian. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 755\u2013763 (2018)"},{"key":"16_CR20","doi-asserted-by":"publisher","first-page":"72","DOI":"10.15827\/0236-235X.109.072-078","volume":"109","author":"Y Rubtsova","year":"2015","unstructured":"Rubtsova, Y.: Constructing a corpus for sentiment classification training. Softw. Syst. 109, 72\u201378 (2015)","journal-title":"Softw. Syst."},{"key":"16_CR21","doi-asserted-by":"crossref","unstructured":"Rusnachenko, N., Loukachevitch, N., Tutubalina, E.: Distant supervision for sentiment attitude extraction. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pp. 1022\u20131030 (2019)","DOI":"10.26615\/978-954-452-056-4_118"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Sahni, T., Chandak, C., Chedeti, N.R., Singh, M.: Efficient twitter sentiment classification using subjective distant supervision. In: 2017 9th International Conference on Communication Systems and Networks (COMSNETS), pp. 548\u2013553. IEEE (2017)","DOI":"10.1109\/COMSNETS.2017.7945451"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Smetanin, S., Komarov, M.: Sentiment analysis of product reviews in Russian using convolutional neural networks. In: 2019 IEEE 21st Conference on Business Informatics (CBI), vol. 1, pp. 482\u2013486. IEEE (2019)","DOI":"10.1109\/CBI.2019.00062"},{"issue":"3","key":"16_CR24","doi-asserted-by":"publisher","first-page":"102484","DOI":"10.1016\/j.ipm.2020.102484","volume":"58","author":"S Smetanin","year":"2021","unstructured":"Smetanin, S., Komarov, M.: Deep transfer learning baselines for sentiment analysis in Russian. Inf. Process. Manage. 58(3), 102484 (2021)","journal-title":"Inf. Process. Manage."},{"key":"16_CR25","unstructured":"Sun, C., Huang, L., Qiu, X.: Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 380\u2013385 (2019)"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Wang, W.Y., Yang, D.: That\u2019s so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using# petpeeve tweets. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2557\u20132563 (2015)","DOI":"10.18653\/v1\/D15-1306"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Wei, J., Zou, K.: EDA: easy data augmentation techniques for boosting performance on text classification tasks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 6383\u20136389 (2019)","DOI":"10.18653\/v1\/D19-1670"},{"issue":"1","key":"16_CR28","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2020","unstructured":"Zhuang, F., et al.: A comprehensive survey on transfer learning. Proc. IEEE 109(1), 43\u201376 (2020)","journal-title":"Proc. IEEE"}],"container-title":["Lecture Notes in Computer Science","Text, Speech, and Dialogue"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-83527-9_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,29]],"date-time":"2021-08-29T23:10:24Z","timestamp":1630278624000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-83527-9_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030835262","9783030835279"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-83527-9_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"TSD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Text, Speech, and Dialogue","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Olomouc","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Czech Republic","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"tsd2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.kiv.zcu.cz\/tsd2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"TSDEngine 3.2","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"101","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,93","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}