[go: up one dir, main page]
Skip to main content
SpringerLink
Log in

Granular Emotion Detection in Social Media Using Multi-Discipline Ensembles

  • Conference paper
  • First Online:
Foundations of Intelligent Systems (ISMIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13515))

Included in the following conference series:

  • 888 Accesses

Abstract

A variety of applications across industry and society have started to adopt emotion detection in short written text as a key enabling component. However, the task of detecting fine-grained emotions (e.g. love, hate, sadness, happiness, etc.) in short texts such as social media remains both challenging and complex. Particularly for high-stakes applications such as health and public safety, there is a need for improved performance. To address the need for more accurate emotion detection in social media (EMDISM), we investigated the performance of ensemble classification approaches, which combine baseline models from machine learning, deep learning, and transformer learning. We evaluated a variety of ensemble approaches in comparison to the best individual component model using an EMDISM Twitter dataset with more than 1.2M samples. Results showed that the most accurate ensemble approaches performed significantly better than the best individual model.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Al-Omari, H., et al.: EmoDet at SemEval-2019 task 3: emotion detection in text using deep learning. In: Proceedings of the 13th International Workshop on Semantic Evaluation (2019)

    Google Scholar 

  2. Araque, O., et al.: Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Syst. Appl. 77, 236–246 (2017)

    Article  Google Scholar 

  3. Asif, M., et al.: Sentiment analysis of extremism in social media from textual information. Telematics Inform. 48, 101345 (2020)

    Article  Google Scholar 

  4. Baziotis, C., et al.: Ntua-slp at semeval-2018 task 3: tracking ironic tweets using ensembles of word and character level attentive RNNs. arXiv:1804.06659 (2018)

  5. Bickerstaffe, A., Zukerman, I.: A hierarchical classifier applied to multi-way sentiment detection. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 62–70. Association for Computational Linguistics (2010)

    Google Scholar 

  6. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)

    MATH  Google Scholar 

  7. Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)

    Article  Google Scholar 

  8. Burnap, P., et al.: Multi-class machine classification of suicide-related communication on twitter. Online Soc. Networks Media 2, 32–44 (2017)

    Article  Google Scholar 

  9. Cao, M.D., Zukerman, I.: Experimental evaluation of a lexicon-and corpus-based ensemble for multi-way sentiment analysis. In: Proceedings of the Australasian Language Technology Association Workshop 2012, pp. 52–60 (2012)

    Google Scholar 

  10. Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)

  11. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)

  12. Clark, K., Luong, M.T., Le, Q.V., Manning, C.D.: ELECTRA: pre-training text encoders as discriminators rather than generators (2020)

    Google Scholar 

  13. 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 

  14. De Choudhury, M., et al.: Predicting depression via social media. In: Seventh international AAAI conference on weblogs and social media (2013)

    Google Scholar 

  15. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding (2019)

    Google Scholar 

  16. Duin, R.P.: Classifiers in almost empty spaces. In: Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, vol. 2, pp. 1–7. IEEE (2000)

    Google Scholar 

  17. Duppada, V., Jain, R., Hiray, S.: Seernet at semeval-2018 task 1: domain adaptation for affect in tweets. arXiv preprint arXiv:1804.06137 (2018)

  18. Efron, B.: The efficiency of logistic regression compared to normal discriminant analysis. J. Am. Stat. Assoc. 70(352), 892–898 (1975)

    Article  MathSciNet  Google Scholar 

  19. Ekman, P.: Basic emotions. In: Handbook of Cognition and Emotion, pp. 45–60 (1999)

    Google Scholar 

  20. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)

    Article  MathSciNet  Google Scholar 

  21. Frye, R.H., Wilson, D.C.: Comparative analysis of transformers to support fine-grained emotion detection in short-text data. In: The Thirty-Fifth International Flairs Conference (2022)

    Google Scholar 

  22. Ghosh, S., Vinyals, O., Strope, B., Roy, S., Dean, T., Heck, L.: Contextual LSTM (CLSTM) models for large scale NLP tasks. arXiv preprint arXiv:1602.06291 (2016)

  23. Gupta, N., Gilbert, M., Fabbrizio, G.D.: Emotion detection in email customer care. Comput. Intell. 29(3), 489–505 (2013)

    Article  MathSciNet  Google Scholar 

  24. Gupta, S.: Applications of sentiment analysis in business. Towards Data Science. https://towardsdatascience.com/applications-of-sentiment-analysis-in-business-b7e660e3de69

  25. Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 10, 993–1001 (1990)

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. Kang, M., Ahn, J., Lee, K.: Opinion mining using ensemble text hidden Markov models for text classification. Expert Syst. Appl. 94, 218–227 (2018)

    Article  Google Scholar 

  28. Khan, J.: Sentiment analysis : Key to empathetic customer service. Ameyo. https://www.ameyo.com/blog/sentiment-analysis-key-to-empathetic-customer-service

  29. Lample, G., Conneau, A.: Cross-lingual language model pretraining (2019)

    Google Scholar 

  30. LeCun, Y., Haffner, P., Bottou, L., Bengio, Y.: Object recognition with gradient-based learning. In: Shape, Contour and Grouping in Computer Vision. LNCS, vol. 1681, pp. 319–345. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-46805-6_19

    Chapter  Google Scholar 

  31. Liu, Y., et al.: Roberta: a robustly optimized Bert pretraining approach (2019)

    Google Scholar 

  32. Oussous, A., Lahcen, A.A., Belfkih, S.: Impact of text pre-processing and ensemble learning on Arabic sentiment analysis. In: Proceedings of the 2nd International Conference on Networking, Information Systems & Security, p. 65. ACM (2019)

    Google Scholar 

  33. Perikos, I., Hatzilygeroudis, I.: Recognizing emotions in text using ensemble of classifiers. Eng. Appl. Artif. Intell. 51, 191–201 (2016)

    Article  Google Scholar 

  34. Pujol, F.A., Mora, H., Pertegal, M.L.: A soft computing approach to violence detection in social media for smart cities. Soft. Comput. 24(15), 11007–11017 (2019). https://doi.org/10.1007/s00500-019-04310-x

    Article  Google Scholar 

  35. Ramadhan, W., Novianty, S.A., Setianingsih, S.C.: Sentiment analysis using multinomial logistic regression. In: 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC), pp. 46–49. IEEE (2017)

    Google Scholar 

  36. Ranganathan, J., Hedge, N., Irudayaraj, A., Tzacheva, A.: Automatic detection of emotions in twitter data-a scalable decision tree classification method. In: Proceedings of the RevOpID 2018 Workshop on Opinion Mining, Summarization and Diversification in 29th ACM Conference on Hypertext and Social Media (2018)

    Google Scholar 

  37. Schapire, R.E.: The strength of weak learnability. Mach. Learn. 5(2), 197–227 (1990)

    Google Scholar 

  38. Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)

    Article  Google Scholar 

  39. Smetanin, S.: Emosense at semeval-2019 task 3: Bidirectional LSTM network for contextual emotion detection in textual conversations. In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 210–214 (2019)

    Google Scholar 

  40. Symeonidis, S., et al.: A comparative evaluation of pre-processing techniques and their interactions for twitter sentiment analysis. Expert Syst. Appl. 110, 298–310 (2018)

    Article  Google Scholar 

  41. Vapnik, V.: The nature of statistical learning theory. Springer, New York (2000). https://doi.org/10.1007/978-1-4757-3264-1

    Book  MATH  Google Scholar 

  42. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)

    Google Scholar 

  43. Walther, C.: Sentiment analysis in marketing: What are you waiting for? CMS Wire. https://www.cmswire.com/digital-marketing/sentiment-analysis-in-marketing-what-are-you-waiting-for/

  44. Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.R.: Glue: a multi-task benchmark and analysis platform for natural language understanding (2019)

    Google Scholar 

  45. Wang, W., et al.: Harnessing twitter “big data” for automatic emotion identification. In: 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Conference on Social Computing, pp. 587–592. IEEE (2012)

    Google Scholar 

  46. Wang, X., et al.: A novel hybrid mobile malware detection system integrating anomaly detection with misuse detection. In: Proceedings of the 6th International Workshop on Mobile Cloud Computing and Services, pp. 15–22. ACM (2015)

    Google Scholar 

  47. Wolfe, J.: Want faster airline customer service? try tweeting. The New York Times. https://www.nytimes.com/2018/11/20/travel/airline-customer-service-twitter.html

  48. Xia, R., Zong, C., Li, S.: Ensemble of feature sets and classification algorithms for sentiment classification. Inf. Sci. 181(6), 1138–1152 (2011)

    Article  Google Scholar 

  49. Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V.: Xlnet: generalized autoregressive pretraining for language understanding (2020)

    Google Scholar 

  50. Yue, L., Chen, W., Li, X., Zuo, W., Yin, M.: A survey of sentiment analysis in social media. Knowl. Inf. Syst. 60(2), 617–663 (2018). https://doi.org/10.1007/s10115-018-1236-4

    Article  Google Scholar 

  51. Yue, T., Chen, C., Zhang, S., Lin, H., Yang, L.: Ensemble of neural networks with sentiment words translation for code-switching emotion detection. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds.) NLPCC 2018. LNCS (LNAI), vol. 11109, pp. 411–419. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99501-4_37

    Chapter  Google Scholar 

  52. Zhang, L., et al.: Deep learning for sentiment analysis: a survey. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 8(4), e1253 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of North Carolina at Charlotte, Charlotte, NC, 28223, USA

    Robert H. Frye & David C. Wilson

Authors
  1. Robert H. Frye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David C. Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Robert H. Frye .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  2. Università della Calabria, Rende, Italy

    Sergio Flesca

  3. Università Federico II di Napoli, Naples, Italy

    Elio Masciari

  4. ICAR-CNR, Rende, Italy

    Giuseppe Manco

  5. University of North Carolina, Charlotte, NC, USA

    Zbigniew W. Raś

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Frye, R.H., Wilson, D.C. (2022). Granular Emotion Detection in Social Media Using Multi-Discipline Ensembles. In: Ceci, M., Flesca, S., Masciari, E., Manco, G., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2022. Lecture Notes in Computer Science(), vol 13515. Springer, Cham. https://doi.org/10.1007/978-3-031-16564-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-16564-1_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16563-4

  • Online ISBN: 978-3-031-16564-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation