Skip to main content
SpringerLink
Log in

Review of Non-English Corpora Annotated for Emotion Classification in Text

  • Conference paper
  • First Online:
Databases and Information Systems (DB&IS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1243))

Included in the following conference series:

Abstract

In this paper we try to systematize the information about the available corpora for emotion classification in text for languages other than English with the goal to find what approaches could be used for low-resource languages with close to no existing works in the field. We analyze the corresponding volume, emotion classification schema, language of each corresponding corpus and methods employed for data preparation and annotation automation. We’ve systematized twenty-four papers representing the corpora and found that corpora were mostly for the most spoken world languages: Hindi, Chinese, Turkish, Arabic, Japanese etc. A typical corpus contained several thousand of manually-annotated entries, collected from a social network, annotated by three annotators each and was processed by a few machine learning methods, such as linear SVM and Naïve Bayes and (more recent ones) a couple of neural networks methods, such as CNN.

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

Access this chapter

Log in via an institution
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

Notes

  1. 1.

    The authors cite Tanaka et al. 2004 (in Japanese) [13] as a source for their schema.

References

  1. Lyakso, E., Frolova, O., Dmitrieva, E., Grigorev, A., Kaya, H., Salah, A.A., Karpov, A.: EmoChildRu: emotional child Russian speech corpus. In: Ronzhin, A., Potapova, R., Fakotakis, N. (eds.) SPECOM 2015. LNCS (LNAI), vol. 9319, pp. 144–152. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23132-7_18

    Chapter  Google Scholar 

  2. Bostan, L.A.M., Klinger, R.: An analysis of annotated corpora for emotion classification in text. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 2104–2119 (2018)

    Google Scholar 

  3. Ekman, P.: An argument for basic emotions. Cogn. Emot. 6(3–4), 169–200 (1992)

    Article  Google Scholar 

  4. Plutchik, R.: A general psychoevolutionary theory of emotion. In: Plutchik, R., Kellerman, H. (eds.) Emotion: Theory, research and experience, Theories of emotion, vol. 1, pp. 3–33. Academic Press, New York (1980)

    Google Scholar 

  5. Ptaszynski, M., Dybala, P., Rzepka, R., Araki, K., Momouchi, Y.: YACIS: a five-billion-word corpus of Japanese blogs fully annotated with syntactic and affective information. In: AISB/IACAP World Congress 2012: Linguistic and Cognitive Approaches to Dialogue Agents, Part of Alan Turing Year 2012, pp. 40–49 (2012)

    Google Scholar 

  6. Lin, K.H.Y., Yang, C., Chen, H.H.: What emotions do news articles trigger in their readers? In: Proceedings of the 30th Annual International ACM SIGIR Conference, pp. 733–734 (2007)

    Google Scholar 

  7. Quan, C., Ren, F.: A blog emotion corpus for emotional expression analysis in Chinese. Comput. Speech Lang. 24(4), 726–749 (2010)

    Article  MathSciNet  Google Scholar 

  8. Kumar, Y., Mahata, D., Aggarwal, S., Chugh, A., Maheshwari, R., Shah, R.R.: BHAAV-A Text Corpus for Emotion Analysis from Hindi Stories (2019). arXiv preprint arXiv:1910.04073

  9. Mehrabian, A.: Basic Dimensions for A General Psychological Theory, pp. 39–53 (1980)

    Google Scholar 

  10. Gohil, L., Patel, D.: Int. J. Innovative Technol. Exploring Eng. (IJITEE) 9(1) (2019). ISSN: 2278-3075

    Google Scholar 

  11. Lapitan, F.R., Batista-Navarro, R.T., Albacea, E.: Crowdsourcing-based annotation of emotions in Filipino and English tweets. In: Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP2016), pp. 74–82 (2016)

    Google Scholar 

  12. Araque, O., Gatti, L., Staiano, J., Guerini, M.: Depechemood ++: a bilingual emotion lexicon built through simple yet powerful techniques. IEEE Trans. Affect. Comput. (2019)

    Google Scholar 

  13. Tanaka, Y., Takamura, H., Okumura, M.: Research concerning facemarks in text based communication. In: Proceedings of the 10th Annual Natural Language Processing Conference (2004). (in Japanese)

    Google Scholar 

  14. Nakamura, A.: Kanjo hyogen jiten [Dictionary of Emotive Expressions]. Tokyodo Publishing, Tokyo (1993). (in Japanese)

    Google Scholar 

  15. Turner, J.H.: On the Origins of Human Emotions: A Sociological Inquiry into the Evolution of Human Affect. Stanford University Press, CA (2000)

    Google Scholar 

  16. Yao, Y., Wang, S., Xu, R., et al.: The construction of an emotion annotated corpus on microblog text. J. Chin. Inf. Process. 28(5), 83–91 (2014). (in Chinese)

    Google Scholar 

  17. Jenarthanan, R., Senarath, Y., Thayasivam, U.: ACTSEA: annotated corpus for Tamil & Sinhala emotion analysis. In: 2019 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, pp. 49–53 (2019)

    Google Scholar 

  18. Das, D., Bandyopadhyay, S.: Labeling emotion in Bengali blog corpus – a fine grained tagging at sentence level. In: Proceedings of the 8th Workshop on Asian Language Resources (ALR8), COLING-2010, pp. 47–55 (2010)

    Google Scholar 

  19. Jain, U., Sandu, A.: Emotion detection from Punjabi text using hybrid support vector machine and maximum entropy algorithm. Int. J. Adv. Res. Comput. Commun. Eng. 4(11), 5 (2015)

    Google Scholar 

  20. Rahman, M., Seddiqui, M.: Comparison of Classical Machine Learning Approaches on Bangla Textual Emotion Analysis, 18 July 2019. arXiv preprint arXiv:1907.07826

  21. Ho, V.A., Nguyen, D.H., Nguyen, D.H., Pham, L.T., Nguyen, D.V., Van Nguyen, K., Nguyen, N.L.: Emotion Recognition for Vietnamese Social Media Text (2019). arXiv preprint arXiv:1911.09339

  22. Ngo, T.L., Linh, P.K., Takeda, H.: A Vietnamese dialog act corpus based on ISO 24617-2 standard. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) (2018)

    Google Scholar 

  23. Boynukalin, Z.: Emotion analysis of Turkish texts by using machine learning methods. Master thesis, Middle East Technical University (2012)

    Google Scholar 

  24. Cakmak, O., Kazemzadeh, A., Can, D., Yildirim, S., Narayanan, S.: Root-word analysis of Turkish emotional language. In: Corpora for Research on Emotion Sentiment & Social Signals (2012)

    Google Scholar 

  25. Burget, R., Karasek, J., Smekal, Z.: Recognition of emotions in Czech newspaper headlines. Radioengineering 20(1), 39–47 (2011)

    Google Scholar 

  26. Nguen, A.: 20 Best German Language Datasets for Machine Learning. https://lionbridge.ai/datasets/20-best-german-language-datasets-for-machine-learning/ (2019). Accessed 18 Jan 2020

  27. Badaro, G., Jundi, H., Hajj, H., El-Hajj, W., Habash, N.: ArSEL: a large scale arabic sentiment and emotion lexicon. In: Proceedings of the 3rd Workshop on Open-Source Arabic Corpora and Processing Tools (2018)

    Google Scholar 

  28. Al-Mahdawi, A., Teahan, W.J.: Automatic emotion recognition in English and Arabic text. Dissertation, Bangor University (2019)

    Google Scholar 

  29. Lee, S.Y.M., Wang, Z.: Emotion in code-switching texts: corpus construction and analysis. ACL-IJCNLP 2015, 91 (2015)

    Google Scholar 

  30. Minato, J., Bracewell, B., Ren, F., Kuroiwa, S.: Japanese emotion corpus analysis and its use for automatic emotion word identification. Eng. Lett. 16(1) (2008)

    Google Scholar 

  31. Hiejima, I.: Japanese-English Emotion Dictionary. Tokyodo Shuppan, Tokyo (1995)

    Google Scholar 

  32. Tocoglu, M.A., Alpkocak, A.: TREMO: a dataset for emotion analysis in Turkish. J. Inf. Sci. 44(6), 848–860 (2018)

    Article  Google Scholar 

  33. Tocoglu, M.A., Ozturkmenoglu, O., Alpkocak, A.A.: Emotion analysis from Turkish tweets using deep neural networks. IEEE Access 7, 183061–183069 (2019)

    Article  Google Scholar 

  34. Staiano, J., Marco, G.: Depechemood: a Lexicon for emotion analysis from crowd-annotated news. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL), pp. 427–433 (2014)

    Google Scholar 

  35. Harikrishna, D., Rao, K.S.: Emotion-specific features for classifying emotions in story text. In: 2016 Twenty Second National Conference on Communication (NCC), pp. 1–4 (2016)

    Google Scholar 

  36. Mohammad, S., Bravo-Marquez, F., Salameh, M., Kiritchenko, S.: Semeval-2018 task 1: affect in tweets. In: Proceedings of the 12th International Workshop on Semantic Evaluation, pp. 1–17 (2018)

    Google Scholar 

  37. Skórzewski, P.: Using book dialogs to extract emotions from texts in Polish. In: Vetulani, Z., Paroubek, P. (eds.) Proceedings of the 9th Language and Technology Conference, pp. 252–257 (2019)

    Google Scholar 

  38. Martinazzo, B., Dosciatti, M.M., Paraiso, E.C.: Identifying emotions in short texts for Brazilian Portuguese. In: IV International Workshop on Web and Text Intelligence (WTI 2012), p. 16 (2011)

    Google Scholar 

  39. Danielewicz-Betz, A., Kaneda, H., Mozgovoy, M., Purgina, M.: Creating English and Japanese twitter corpora for emotion analysis. People 1634, 5869 (2015)

    Google Scholar 

  40. Wang, Z.: Segment-based fine-grained emotion detection for Chinese text. In: Proceedings of The Third CIPS-SIGHAN Joint Conference on Chinese Language Processing, pp. 52–60 (2014)

    Google Scholar 

  41. Ptaszynski, M., Maciejewski, J., Dybala, P., Rzepka, R., Araki, K.: CAO: fully automatic emoticon analysis system. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI-10), pp. 1026–1032 (2010)

    Google Scholar 

  42. Ptaszynski, M., Dybala, P., Rzepka, R., Araki, K.: Affecting corpora: experiments with automatic affect annotation system - a case study of the 2channel forum. In: Proceedings of the Conference of the Pacific Association for Computational Linguistics (PACLING-09), pp. 223–228 (2009)

    Google Scholar 

Download references

Acknowledgements

The research has been supported by the European Regional Development Fund within the joint project of SIA TILDE and University of Latvia “Multilingual Artificial Intelligence Based Human Computer Interaction” No.1.1.1.1/18/A/148.

Author information

Authors and Affiliations

  1. University of Latvia, Riga, Latvia

    Viktorija Leonova

Authors
  1. Viktorija Leonova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Viktorija Leonova .

Editor information

Editors and Affiliations

  1. Department of Computer Systems, Tallinn University of Technology, Tallinn, Estonia

    Tarmo Robal

  2. Department of Software Science, Tallinn University of Technology, Tallinn, Estonia

    Hele-Mai Haav

  3. Department of Software Science, Tallinn University of Technology, Tallinn, Estonia

    Jaan Penjam

  4. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Raimundas Matulevičius

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Leonova, V. (2020). Review of Non-English Corpora Annotated for Emotion Classification in Text. In: Robal, T., Haav, HM., Penjam, J., Matulevičius, R. (eds) Databases and Information Systems. DB&IS 2020. Communications in Computer and Information Science, vol 1243. Springer, Cham. https://doi.org/10.1007/978-3-030-57672-1_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-57672-1_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57671-4

  • Online ISBN: 978-3-030-57672-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation