“I don’t speak English well but if I met Joanne Rowling, I’d thank her for taking me in her book and tell her that it’s a very interesting fairytale,” Dunya said. “You feel pride and joy when you hold a book with your own illustrations in your hands,” the young artist told Sever.Realii.Īs a prize, Dunya - who had never read Harry Potter or other Rowling books prior to “The Ickabog” - was offered 10 books of her own choice in addition to a signed copy of the latest novel. Her illustration of the greedy wine-drinking monster is featured on page 107 of “The Ickabog.” I danced for joy,” Dunya told the Sever.Realii news website, a northwestern Russia affiliate of the U.S.-funded RFE/RL outlet.ĭunya, who said she aspires to follow in her parents’ footsteps as an artist when she grows up, worked on her version of the character Lord Spittleworth in her grandmother’s village near the Latvian border in the Pskov region. National Research University Higher School of Economics, St.“Mama had received a text message close to evening saying that I won the contest. Krasovskii Institute of Mathematics and Mechanics, Yekaterinburg, Russia National Research University Higher School of Economics, Moscow, Russia University of Ljubljana, Ljubljana, Slovenia In: Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence (2018) Zahiri, S.M., Choi, J.D.: Emotion detection on tv show transcripts with sequence-based convolutional neural networks. Seyeditabari, A., Tabari, N., Gholizadeh, S., Zadrozny, W.: Emotion detection in text: focusing on latent representation (2019). In Komp'yuternaya lingvistika i intellektual'nye tekhnologii: Po materialam yezhegodnoy Mezhdunarodnoy konferentsii «Dialog» (Bekasovo, 25–29 maya 2011 g.). Pazel'skaya, A.G., Solov'ev, A.N.: Metod opredeleniya emotsiy v tekstakh na russkom yazyke. Mohammad, S.M., Zhu, X., Kiritchenko, S., Martin, J.: Sentiment, emotion, purpose, and style in electoral tweets. Mohammad, S.M., Kiritchenko, S.: Using hashtags to capture fine emotion categories from tweets. Mehendale, N.: Facial emotion recognition using convolutional neural networks (FERC). Papers from the Annual International Conference “Dialogue”, no. In: Computational Linguistics and Intellectual Technologies. A.: Automatic emotion identification in russian text messages. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. Klinger, R.: An analysis of annotated corpora for emotion classification in text. Hasan, M., Rundensteiner, E., Agu, E.: Automatic emotion detection in text streams by analyzing twitter data. Gupta, U., Chatterjee, A., Srikanth, R., Agrawal, P.: A sentiment-and-semantics-based approach for emotion detection in textual conversations (2017). Seriya: Lingvistika i mezhkul'turnaya kommunikatsiya (3) (2015) You can preview every available voice in the Studio. Check the text for accuracy and choose a suitable voice from Wavel s list of 250+ high-quality voices. The Studio automatically transcribes the text to Speech. Vestnik Voronezhskogo gosudarstvennogo universiteta. Open Wavel Studio and select the language preference section to upload the audio file. Gudovskikh, D.V., Moloshnikov, I.A., Rybka, R.B.: Analiz emotivnosti tekstov na osnove psikholingvisticheskikh markerov s opredeleniem morfologicheskikh svoystv. Gaind, B., Syal, V., Padgalwar, S.: Emotion detection and analysis on social media (2019). 39–48 (2019)Įkman, P.: Expression and the nature of emotion. In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. IEEE (2016)Ĭhatterjee, A., Narahari, K.N., Joshi, M., Agrawal, P.: Semeval-2019 task 3: emocontext contextual emotion detection in text. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 718–728 (2017)Ĭanales, L., Strapparava, C., Boldrini, E., Martnez-Barco, P.: Exploiting a bootstrapping approach for automatic annotation of emotions in texts. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. KeywordsĪbdul-Mageed, M., Ungar, L.: Emonet: Fine-grained emotion detection with gated recurrent neural networks. Additionally, we perform error analysis and discover ways to improve the model in the future. ![]() In this paper, we also report on the importance of different linguistic features of the text messages for the task of automatic emotive analysis. ![]() ![]() As a result, an emotion classification model demonstrating near-human performance was designed. ![]() Furthermore, the level of expressiveness was considered as well. This approach relies on morphological, lexical, and stylistic features of the text. This paper proposes an integrated approach to text-based emotion classification combining linguistic methods and machine learning. Emojis contained in the text messages were used to annotate the data for emotions expressed. For this purpose, a new large dataset of text messages from the most popular Russian messaging/social networking services (Telegram, VK) was compiled semi-automatically. In this paper, we address the issue of identifying emotions in Russian informal text messages.
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