7/15/2023 0 Comments Pinpoint emojiMikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. Kralj Novak, P., Smailović, J., Sluban, B., Mozetič, I.: Sentiment of emojis. Hill, F., Cho, K., Korhonen, A., Bengio, Y.: Learning to understand phrases by embedding the dictionary. Guibon, G., Ochs, M., Bellot, P.: From emoji usage to categorical emoji prediction. Association for Computational Linguistics, Austin, November 2016. In: Workshop on NLP for Social Media, pp. 4171–4186 (2019)Įisner, B., Rocktäschel, T., Augenstein, I., Bošnjak, M., Riedel, S.: emoji2vec: learning emoji representations from their description. AIJ 228, 66–94 (2015)ĭevlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 117–125 (2018)ĭerrac, J., Schockaert, S.: Inducing semantic relations from conceptual spaces: a data-driven approach to plausible reasoning. ELRA (European Language Resources Association) (2016)Ĭhen, Y., Yuan, J., You, Q., Luo, J.: Twitter sentiment analysis via bi-sense emoji embedding and attention-based LSTM. 11 (2017)īarbieri, F., Ronzano, F., Saggion, H.: What does this emoji mean? A vector space skip-gram model for Twitter emojis. IJTMIS 2(1), 102–112 (2020)Īi, W., Lu, X., Liu, X., Wang, N., Huang, G., Mei, Q.: Untangling emoji popularity through semantic embeddings. Ahanin, Z., Ismail, M.A.: Feature extraction based on fuzzy clustering and emoji embeddings for emotion classification.
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