Open Access
ITM Web Conf.
Volume 53, 2023
2nd International Conference on Data Science and Intelligent Applications (ICDSIA-2023)
Article Number 02004
Number of page(s) 16
Section Machine Learning / Deep Learning
Published online 01 June 2023
  1. Digital 2023 Global Overview Report, [Google Scholar]
  2. Cappallo, Spencer, Thomas Mensink, and Cees GM Snoek. “Image2emoji: Zero-shot emoji prediction for visual media.” Proceedings of the 23rd ACM international conference on Multimedia. 2015. [Google Scholar]
  3. Al-Halah, Ziad, et al. “Smile, be happy:) emoji embedding for visual sentiment analysis.” Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops. 2019. [Google Scholar]
  4. Most Popular. “[Google Scholar]
  5. Tomihira, Toshiki, et al. “What does your tweet emotion mean? Neural emoji prediction for sentiment analysis.” Proceedings of the 20th international conference on information integration and web-based applications & services. 2018. [Google Scholar]
  6. Peng, Dunlu, and Huimin Zhao. “Seq2Emoji: A hybrid sequence generation model for short text emoji prediction.” Knowledge-Based Systems 214 (2021): 106727. [CrossRef] [Google Scholar]
  7. Barbieri, Francesco, et al. “Multimodal emoji prediction.” arXiv preprint arXiv:1803.02392 (2018). [Google Scholar]
  8. Ramaswamy, Swaroop, et al. “Federated learning for emoji prediction in a mobile keyboard.” arXiv preprint arXiv:1906.04329 (2019). [Google Scholar]
  9. Ranjan, Ritwik, and Palak Yadav. “Emoji Prediction using LSTM and Naive Bayes.” TENCON 2021-2021 IEEE Region 10 Conference (TENCON). IEEE, 2021. [Google Scholar]
  10. Chandra, Papel, et al. “Contextual Emotion Detection in Text using Deep Learning and Big Data.” 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA). IEEE, 2022. [Google Scholar]
  11. Acheampong, Francisca Adoma, Chen Wenyu, and Henry Nunoo‐Mensah. “Text‐based emotion detection: Advances, challenges, and opportunities.” Engineering Reports 2.7 (2020): e12189. [Google Scholar]
  12. Mittal, Mamta, Maanak Arora, and Tushar Pandey. “Emoticon prediction on textual data using stacked LSTM model.” Communication and Intelligent Systems: Proceedings of ICCIS 2019. Springer Singapore, 2020. [Google Scholar]
  13. Cappallo, Spencer, et al. “New modality: Emoji challenges in prediction, anticipation, and retrieval.” IEEE Transactions on Multimedia 21.2 (2018): 402-415. [Google Scholar]
  14. Wu, Chuhan, et al. “Tweet emoji prediction using hierarchical model with attention.” Proceedings of the 2018 ACM international joint conference and 2018 international symposium on pervasive and ubiquitous computing and wearable computers. 2018. [Google Scholar]
  15. Guibon, Gaël, Magalie Ochs, and Patrice Bellot. “From emoji usage to categorical emoji prediction.” 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLING 2018). 2018. [Google Scholar]

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