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2017 | OriginalPaper | Buchkapitel

Joint Emoji Classification and Embedding Learning

verfasst von : Xiang Li, Rui Yan, Ming Zhang

Erschienen in: Web and Big Data

Verlag: Springer International Publishing

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Abstract

Under conversation scenarios, emoji is widely used to express humans’ feelings, which greatly enriches the representation of plain text. Plentiful utterances with emoji are produced by humans manually in social media platforms every day, which make emoji great influence on the human life. For the academic community, researchers are always with the help of utterances including emoji as annotated data to work on sentiment analysis, yet lack of adequate attention to emoji itself. The challenges lie in how to discriminate so many different kinds of emoji, especially for those with similar meanings, which make this problem quite different from traditional sentiment analysis. In this paper, in order to gain an insight into emoji, we propose a matching architecture using deep neural networks to jointly learn emoji embeddings and make classification. In particular, we use a convolutional neural network to get the embedding of the utterance and match it with the embedding of the corresponding emoji, to obtain its best classification, and otherwise also train the emoji embeddings. Experiments based on a massive dataset demonstrate the effectiveness of our proposed approach better than traditional softmax methods in terms of p@1, p@5 and MRR evaluation metrics. Then a test of human experience shows the performance could meet the requirement of practice systems.

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Vorheriges Kapitel Event2vec: Learning Representations of Events on Temporal Sequences

Nächstes Kapitel Target-Specific Convolutional Bi-directional LSTM Neural Network for Political Ideology Analysis

http://www.weibo.com.

We notice a piece of parallel work [2], which is an application named Dango on Android platform, and also suggest emoji for conversation between humans. However, we are the first to conduct scientific experiments, showing the effectiveness of matching.

http://www.weibo.com.

https://en.wikipedia.org/wiki/Mean_reciprocal_rank.

Tang, D., Wei, F., Qin, B., Zhou, M., Liu, T.: Building large-scale twitter-specific sentiment lexicon: a representation learning approach. In: Proceedings of the 25th International Conference on Computational Linguistics, pp. 172–182 (2014)

Snelgrove, X.: http://getdango.com/emoji-and-deep-learning.html

Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: International Conference on Language Resources and Evaluation, pp. 1320–1326 (2010)

Yan, J.L.S., Turtle, H.R.: Exploring fine-grained emotion detection in tweets. In: Proceedings of NAACL-HLT, pp. 73–80 (2016)

Wu, F., Song, Y., Huang, Y.: Microblog sentiment classification with contextual knowledge regularization. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, pp. 2332–2338 (2015)

Deng, L., Wiebe, J., Choi, Y.: Joint inference and disambiguation of implicit sentiments via implicature constraints. In: Proceedings of the 25th International Conference on Computational Linguistics, pp. 79–88 (2014)

Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347–354 (2005)

Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 1555–1565 (2014)

Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 1631–1642 (2013)

10.

dos Santos, C.N., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of the 25th International Conference on Computational Linguistics, pp. 69–78 (2014)

11.

Mou, L., Peng, H., Li, G., Xu, Y., Zhang, L., Jin, Z. Discriminative neural sentence modeling by tree-based convolution. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 2315–2325 (2015)

12.

Beck, D., Cohn, T., Specia, L.: Joint emotion analysis via multi-task Gaussian processes. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 1798–1803 (2014)

13.

Wang, Z., Lee, S.Y.M., Li, S., Zhou, G.: Emotion detection in code-switching texts via bilingual and sentimental information. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, pp. 763–768 (2015)

14.

Chang, Y.C., Chen, C.C., Hsieh, Y.L., Chen, C.C., Hsu, W.L.: Linguistic template extraction for recognizing reader-emotion and emotional resonance writing assistance. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, pp. 775–780 (2015)

15.

Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 417–424 (2002)

16.

Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)CrossRef

17.

Kouloumpis, E., Wilson, T., Moore, J.D.: Twitter sentiment analysis: the good the bad and the omg!. In: Proceedings of the 15th International AAAI Conference on Web and Social Media, pp. 538–541 (2011)

18.

Lambert, P.: Aspect-level cross-lingual sentiment classification with constrained SMT. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, pp. 781–787 (2015)

19.

Wang, X., Wei, F., Liu, X., Zhou, M., Zhang, M.: Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach. Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1031–1040 (2011)

20.

Yang, M., Peng, B., Chen, Z., Zhu, D., Chow, K.P.: A topic model for building fine-grained domain-specific emotion lexicon. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 421–426 (2014)

21.

Zhou, H., Chen, L., Shi, F., Huang, D.: Learning bilingual sentiment word embeddings for cross-language sentiment classification. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, pp. 430–440 (2015)

22.

Dong, L., Wei, F., Zhou, M., Xu, K.: Adaptive multi-compositionality for recursive neural models with applications to sentiment analysis. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp. 1537–1543 (2014)

23.

Ren, Y., Zhang, Y., Zhang, M., Ji, D.: Improving twitter sentiment classification using topic-enriched multi-prototype word embeddings. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp. 3038–3044 (2016)

24.

Zhang, M., Zhang, Y., Vo, D.T.: Gated neural networks for targeted sentiment analysis. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp. 3087–3093 (2016)

25.

Vanzo, A., Croce, D., Basili, R.: A context-based model for sentiment analysis in twitter. In: Proceedings of the 25th International Conference on Computational Linguistics, pp. 2345–2354 (2014)

26.

Ren, Y., Zhang, Y., Zhang, M., Ji, D.: Context-sensitive twitter sentiment classification using neural network. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp. 215–221 (2016)

27.

Li, S., Huang, L., Wang, R., Zhou, G.: Sentence-level emotion classification with label and context dependence. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, pp. 1045–1053 (2015)

28.

Cappallo, S., Mensink, T., Snoek, C.G.: Image2emoji: Zero-shot emoji prediction for visual media. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 1311–1314 (2015)

29.

Hu, B., Lu, Z., Li, H., Chen, Q.: Convolutional neural network architectures for matching natural language sentences. In: Annual Conference on Neural Information Processing Systems, pp. 2042–2050 (2014)

30.

Yan, R., Song, Y., Wu, H.: Learning to Respond with Deep Neural Networks for Retrieval based Human-Computer Conversation System. In: Proceedings of SIGIR, pp. 55–64 (2016)

31.

Socher, R., Huang, E.H., Pennin, J., Ng, A.Y., Manning, C.D.: Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: Annual Conference on Neural Information Processing Systems, pp. 801–809 (2011)

32.

Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 1724–1734 (2014)

33.

Tang, Y.: Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239 (2013)

34.

Fleiss, J.L.: Measuring nominal scale agreement among many raters. Psychol. Bull. 76(5), 378 (1971)CrossRef

35.

Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3(Feb), 1137–1155 (2003)MATH

36.

Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)

37.

Novak, P.K., Smailović, J., Sluban, B., Mozetič, I.: Sentiment of emojis. PloS One 10(12), e0144296 (2015)CrossRef

38.

Babu, G.S., Zhao, P., Li, X.L.: Deep convolutional neural network based regression approach for estimation of remaining useful life. In: Proceedings of the 21st International Conference on Database Systems for Advanced Applications, pp. 214–228 (2016)

39.

Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)

40.

Shang, L., Lu, Z., Li, H.: Neural responding machine for short-text conversation. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, pp. 1577–1586 (2015)

41.

Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef

42.

Sordoni, A., Galley, M., Auli, M., Brockett, C., Ji, Y., Mitchell, M., Nie, J.Y., Gao, J., Dolan, B.: A neural network approach to context-sensitive generation of conversational responses. In: Proceedings of NAACL-HLT, pp. 196–205 (2015)

43.

Mou, L., Song, Y., Yan, R., Li, G., Zhang, L., Jin, Z.: Sequence to backward and forward sequences: a content-introducing approach to generative short-text conversation. In: Proceedings of the 26th International Conference on Computational Linguistics, pp. 3349–3358 (2016)

44.

Hogenboom, A., Bal, D., Frasincar, F., Bal, M., de Jong, F., Kaymak, U.: Exploiting emoticons in polarity classification of text. J. Web Eng. 14, 22–40 (2015)

45.

Wang, Y., Feng, S., Wang, D., Yu, G., Zhang, Y.: Multi-label chinese microblog emotion classification via convolutional neural network. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds.) APWeb 2016. LNCS, vol. 9931, pp. 567–580. Springer, Cham (2016). doi:10.1007/978-3-319-45814-4_46 CrossRef

46.

Zhao, Z., Liu, T., Hou, X., Li, B., Du, X.: Distributed text representation with weighting scheme guidance for sentiment analysis. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds.) APWeb 2016. LNCS, vol. 9931, pp. 41–52. Springer, Cham (2016). doi:10.1007/978-3-319-45814-4_4 CrossRef

Titel: Joint Emoji Classification and Embedding Learning
verfasst von: Xiang Li
Rui Yan
Ming Zhang
Verlag: Springer International Publishing
Buch: Web and Big Data
Print ISBN: 978-3-319-63563-7

Electronic ISBN: 978-3-319-63564-4

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-63564-4_4

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