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Detecting Negative Sentiment on Sarcastic Tweets for Sentiment Analysis

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Artificial Neural Networks and Machine Learning – ICANN 2023 (ICANN 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14263))

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Abstract

Sentiment Analysis (SA) is a fundamental and practical research problem in the field of natural language understanding(NLU). Meanwhile, sarcasm detection is a task to detect sarcasm in textual data. Previous works solve these two problems independently and neglect the fact that sarcasm is omnipresent and non-negligible during sentiment analysis. To explore this issue, in this paper, we formulate a general sentiment Analysis (GSA) problem where sarcastic data could be input and point out the limitations of current mainstream frameworks by systematic investigation. To address the GSA problem, we propose a sarcasm-perceivable SA (Sp-SA) training framework to train a model that is robust to sarcasm and able to achieve state-of-the-art performance. Extensive experiments and detailed analysis demonstrate our Sp-SA framework’s effectiveness and interpretability. Code and dataset will be publicly available for future research.

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References

  1. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.J.: Sentiment analysis of twitter data. In: Proceedings of the Workshop on Language in Social Media (LSM 2011), pp. 30–38 (2011)

    Google Scholar 

  2. Bermingham, A., Smeaton, A.F.: Classifying sentiment in microblogs: is brevity an advantage? In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1833–1836 (2010)

    Google Scholar 

  3. Brody, S., Diakopoulos, N.: Cooooooooooooooollllllllllllll!!!!!!!!!!!!!! using word lengthening to detect sentiment in microblogs. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 562–570 (2011)

    Google Scholar 

  4. Da Silva, N.F., Hruschka, E.R., Hruschka, E.R., Jr.: Tweet sentiment analysis with classifier ensembles. Decis. Support Syst. 66, 170–179 (2014)

    Article  Google Scholar 

  5. Dadu, T., Pant, K.: Sarcasm detection using context separators in online discourse. In: Proceedings of the Second Workshop on Figurative Language Processing, pp. 51–55. Association for Computational Linguistics, Online (2020)

    Google Scholar 

  6. Davidov, D., Tsur, O., Rappoport, A.: Enhanced sentiment learning using Twitter hashtags and smileys. In: Coling 2010: Posters, pp. 241–249. Coling 2010 Organizing Committee, Beijing, China (2010)

    Google Scholar 

  7. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics (2019)

    Google Scholar 

  8. Giachanou, A., Crestani, F.: Like it or not: a survey of twitter sentiment analysis methods. ACM Comput. Surv. 49(2), 1–41 (2016)

    Article  Google Scholar 

  9. González-Ibáñez, R., Muresan, S., Wacholder, N.: Identifying sarcasm in Twitter: a closer look. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 581–586. Association for Computational Linguistics, Portland, Oregon, USA (2011)

    Google Scholar 

  10. Gyanendro Singh, L., Mitra, A., Ranbir Singh, S.: Sentiment analysis of tweets using heterogeneous multi-layer network representation and embedding. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 8932–8946. Association for Computational Linguistics, Online (2020)

    Google Scholar 

  11. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. KDD 2004, Association for Computing Machinery, New York, NY, USA (2004)

    Google Scholar 

  12. Hu, Z., Ma, X., Liu, Z., Hovy, E., Xing, E.: Harnessing deep neural networks with logic rules. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2410–2420. Association for Computational Linguistics, Berlin, Germany (2016)

    Google Scholar 

  13. Joshi, A., Bhattacharyya, P., Carman, M.J.: Automatic sarcasm detection: a survey. ACM Comput. Surv. 50(5), 1–22 (2017)

    Article  Google Scholar 

  14. Khare, A., Gangwar, A., Singh, S., Prakash, S.: Sentiment analysis and sarcasm detection of indian general election tweets. arXiv preprint arXiv:2201.02127 (2022)

  15. Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: the good the bad and the omg! In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 5, pp. 538–541 (2011)

    Google Scholar 

  16. Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Aggarwal, C., Zhai, C. (eds.) Mining Text Data, pp. 415–463. Springer, Boston (2012). https://doi.org/10.1007/978-1-4614-3223-4_13

    Chapter  Google Scholar 

  17. Ma, W., Lou, R., Zhang, K., Wang, L., Vosoughi, S.: GradTS: a gradient-based automatic auxiliary task selection method based on transformer networks. In: Proceedings of EMNLP 2021, pp. 5621–5632. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic (2021)

    Google Scholar 

  18. Majumder, N., Poria, S., Peng, H., Chhaya, N., Cambria, E., Gelbukh, A.: Sentiment and sarcasm classification with multitask learning. IEEE Intell. Syst. 34(03), 38–43 (2019)

    Article  Google Scholar 

  19. Nguyen, D.Q., Vu, T., Nguyen, A.T.: BERTweet: a pre-trained language model for English Tweets (2020)

    Google Scholar 

  20. Rajadesingan, A., Zafarani, R., Liu, H.: Sarcasm detection on twitter: A behavioral modeling approach. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. pp. 97–106. WSDM 2015, Association for Computing Machinery, New York, NY, USA (2015)

    Google Scholar 

  21. Riloff, E., Qadir, A., Surve, P., De Silva, L., Gilbert, N., Huang, R.: Sarcasm as contrast between a positive sentiment and negative situation. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 704–714 (2013)

    Google Scholar 

  22. Rosenthal, S., Farra, N., Nakov, P.: SemEval-2017 task 4: sentiment analysis in twitter. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 502–518 (2017)

    Google Scholar 

  23. Rosenthal, S., Ritter, A., Nakov, P., Stoyanov, V.: SemEval-2014 task 9: sentiment analysis in Twitter. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 73–80. Association for Computational Linguistics, Dublin, Ireland (2014)

    Google Scholar 

  24. Sun, L., Zhang, K., Ji, F., Yang, Z.: TOI-CNN: a solution of information extraction on Chinese insurance policy. In: Proceedings of the NAACL-HLT 2019, pp. 174–181. Association for Computational Linguistics, Minneapolis, Minnesota (2019)

    Google Scholar 

  25. Sykora, M., Elayan, S., Jackson, T.W.: A qualitative analysis of sarcasm, irony and related# hashtags on twitter. Big Data Soc. 7(2), 2053951720972735 (2020)

    Article  Google Scholar 

  26. Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 1556–1566. Association for Computational Linguistics, Beijing, China (2015)

    Google Scholar 

  27. Van Hee, C., Lefever, E., Hoste, V.: Semeval-2018 task 3: Irony detection in English tweets. In: Proceedings of The 12th International Workshop on Semantic Evaluation, pp. 39–50 (2018)

    Google Scholar 

  28. Vosoughi, S., Zhou, H., Roy, D.: Enhanced Twitter sentiment classification using contextual information. In: Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 16–24. Association for Computational Linguistics, Lisboa, Portugal (Sep 2015)

    Google Scholar 

  29. Zhang, K., Gutiérrez, B.J., Su, Y.: Aligning instruction tasks unlocks large language models as zero-shot relation extractors. In: Findings of ACL 2023 (2023)

    Google Scholar 

  30. Zhang, K., Sun, L., Ji, F.: A TOI based CNN with location regression for insurance contract analysis. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2019)

    Google Scholar 

  31. Zhang, K., et al.: Open hierarchical relation extraction. In: Proceedings of NAACL 2021 (2021)

    Google Scholar 

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Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Qingyuan Li & Ruichen Xia

  2. Department of Computer Science, Hangzhou City University, HangZhou, China

    Qingyuan Li & Lin Sun

  3. Department of Computer Science and Engineering, Ohio State University, Columbus, USA

    Kai Zhang

  4. Institute of Computer innovation, Zhejiang University, Hangzhou, China

    Ruichen Xia

Authors
  1. Qingyuan Li
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  2. Kai Zhang
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  3. Lin Sun
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  4. Ruichen Xia
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Corresponding author

Correspondence to Ruichen Xia .

Editor information

Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  3. Lancaster University, Lancaster, UK

    Plamen Angelov

  4. Teesside University, Middlesbrough, UK

    Chrisina Jayne

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Li, Q., Zhang, K., Sun, L., Xia, R. (2023). Detecting Negative Sentiment on Sarcastic Tweets for Sentiment Analysis. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14263. Springer, Cham. https://doi.org/10.1007/978-3-031-44204-9_40

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  • DOI: https://doi.org/10.1007/978-3-031-44204-9_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44203-2

  • Online ISBN: 978-3-031-44204-9

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