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Classification-Based Emoji Recommendation for User Social Networks

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Web and Big Data (APWeb-WAIM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11809))

Abstract

Emoji recommendation focuses on solving time-consuming problem of finding emoji in social media platforms. The existing research works about emoji recommendation mainly take emoji as classification labels to train the relationship between emojis and texts by machine learning methods. However, they do not consider the one-to-many relationship between texts and emojis, nor do they pay attention to users’ motivation to use emojis in real social media. At the same time, few studies make emoji recommendation based on Chinese corpus. This paper divides million-level Chinese micro-blog corpus into different context sets according to emojis, then propose a method to generate emoji-related features by analysis, finally a classification-based recommendation method is proposed by integrating these features. The experimental results show that the proposed method significantly improves the accuracy of emoji recommendation in social media platforms.

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References

  1. Barbieri, F., Ballesteros, M., Saggion, H.: Are emojis predictable? In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pp. 105–111 (2017)

    Google Scholar 

  2. Effrosynidis, D.: DUTH at SemEval-2018 Task 2: emoji prediction in tweets. In: Proceedings of the 12th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT, New Orleans, Louisiana, USA, 5–6 June 2018, pp. 466–469 (2018)

    Google Scholar 

  3. Chen, J., Yang, D., Li, X., Chen, W., Wang, T.: Peperomia at SemEval-2018 Task 2 : vector similarity based approach for emoji prediction. In: Proceedings of the 12th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT, New Orleans, Louisiana, USA, 5–6 June 2018, pp. 428–432 (2018)

    Google Scholar 

  4. Rama, T.: Tübingen-Oslo at SemEval-2018 Task 2 : SVMs perform better than RNNs at emoji prediction. In: Proceedings of the 12th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT, New Orleans, Louisiana, USA, 5–6 June 2018, pp. 34–38 (2018)

    Google Scholar 

  5. Wang, Z., Pedersen, T.: UMDSub at SemEval-2018 Task 2 : multilingual emoji prediction multi-channel convolutional neural network on subword embedding. In: Proceedings of the 12th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT, New Orleans, Louisiana, USA, 5–6 June 2018, pp. 395–399 (2018)

    Google Scholar 

  6. Xie, R., Liu, Z., Yan, R., Sun, M.: Neural emoji recommendation in dialogue systems. arXiv e-prints (2016). http://arxiv.org/abs/1612.04609

  7. Wu, C., Wu, F., Wu, S., Yuan, Z.: THU NGN at SemEval-2018 Task 2 : residual CNN-LSTM network with attention for English emoji prediction. In: Proceedings of the 12th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT, New Orleans, Louisiana, USA, 5–6 June 2018, pp. 410–414 (2018)

    Google Scholar 

  8. Guibon, G., Ochs, M., Bellot, P.: Prédiction automatique d’ emojis sentimentaux. In: The 14th French Information Retrieval Conference, Marseille, France, 29–31 March 2017, pp. 59–74 (2017)

    Google Scholar 

  9. Choudhary, N., Singh, R., Rao, V.A., Shrivastava, M.: Twitter corpus of resource-scarce languages for sentiment analysis and multilingual emoji prediction. In: Proceedings of the 27th International Conference on Computational Linguistics, COLING, Santa Fe, New Mexico, USA, 20–26 August 2018, pp. 1570–1577 (2018). http://www.aclweb.org/anthology/C18-1133

  10. Baziotis, C., Athanasiou, N., Paraskevopoulos, G., Ellinas, N., Kolovou, A., Potamianos, A.: NTUA-SLP at SemEval-2018 Task 2 : predicting emojis using RNNs with context-aware attention. In: Proceedings of the 12th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT, New Orleans, Louisiana, USA, 5–6 June 2018, pp. 438–444 (2018)

    Google Scholar 

  11. Liu, Man.: EmoNLP at SemEval-2018 Task 2 : english emoji prediction with gradient boosting regression tree method and bidirectional LSTM. In: Proceedings of the 12th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT, New Orleans, Louisiana, USA, 5–6 June 2018, pp. 390–394 (2018)

    Google Scholar 

  12. Kaye, L.K., Wall, H.J., Malone, S.A.: “Turn that frown upside-down”: a contextual account of emoticon usage on different virtual platforms. Comput. Hum. Behav. 60, 463–467 (2016). https://doi.org/10.1016/j.chb.2016.02.088

    Article  Google Scholar 

  13. Hu, T., Guo, H., Sun, H., Nguyen, T.V.T., Luo, J.: Spice up your chat: the intentions and sentiment effects of using emojis. In: Proceedings of the Eleventh International Conference on Web and Social Media, ICWSM, Canada, 15–18 May 2017, pp. 102–111 (2017). http://arxiv.org/abs/1703.02860

  14. Ma, X.: From internet memes to emoticon engineering: insights from the baozou comic phenomenon in China. In: Human-Computer Interaction. Novel User Experiences - 18th International Conference, HCI International, Toronto, ON, Canada, 17–22 July 2016, pp. 15–27 (2016). https://doi.org/10.1007/978-3-319-39513-5

  15. Zhou, R., Hentschel, J., Kumar, N.: Goodbye text, hello emoji : mobile communication on WeChat in China. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, Denver, CO, USA, 06–11 May 2017, pp. 748–759 (2017)

    Google Scholar 

  16. Salton, G., Yu, C.T.: On the construction of effective vocabularies for information retrieval. In: Proceedings of the 1973 meeting on Programming Languages and Information Retrieval, Gaithersburg, Maryland, USA, 4–6 November 1973, pp. 48–60 (1973)

    Google Scholar 

  17. Wu, Y., Li, Y., Hao, G.: A web-based theme-related word set construction algorithm. In: Web and Big Data - APWeb-WAIM 2018 International Workshops: MWDA, BAH, KGMA, DMMOOC, DS, Macau, China, 23–25 July 2018, pp. 188-200 (2018)

    Google Scholar 

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Acknowledgement

This research was supported by the Natural Science Foundation of Tianjin (No. 15JCYBJC46500), the Training plan of Tianjin University Innovation Team (No. TD13-5025), and the Major Project of Tianjin Smart Manufacturing (No. 15ZXZNCX00050).

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Authors and Affiliations

  1. Tianjin University of Technology, 300384, Tianjin, China

    Yuan Wang, Yukun Li & Fenglian Liu

  2. Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin, China

    Yukun Li

  3. Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin, China

    Fenglian Liu

Authors
  1. Yuan Wang
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  2. Yukun Li
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  3. Fenglian Liu
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Corresponding author

Correspondence to Yukun Li .

Editor information

Editors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Jingkuan Song

  2. Massey University, Auckland, New Zealand

    Xiaofeng Zhu

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Wang, Y., Li, Y., Liu, F. (2019). Classification-Based Emoji Recommendation for User Social Networks. In: Song, J., Zhu, X. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11809. Springer, Cham. https://doi.org/10.1007/978-3-030-33982-1_2

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  • DOI: https://doi.org/10.1007/978-3-030-33982-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33981-4

  • Online ISBN: 978-3-030-33982-1

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