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A survey of state-of-the-art approaches for emotion recognition in text

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Abstract

Emotion recognition in text is an important natural language processing (NLP) task whose solution can benefit several applications in different fields, including data mining, e-learning, information filtering systems, human–computer interaction, and psychology. Explicit emotion recognition in text is the most addressed problem in the literature. The solution to this problem is mainly based on identifying keywords. Implicit emotion recognition is the most challenging problem to solve because such emotion is typically hidden within the text, and thus, its solution requires an understanding of the context. There are four main approaches for implicit emotion recognition in text: rule-based approaches, classical learning-based approaches, deep learning approaches, and hybrid approaches. In this paper, we critically survey the state-of-the-art research for explicit and implicit emotion recognition in text. We present the different approaches found in the literature, detail their main features, discuss their advantages and limitations, and compare them within tables. This study shows that hybrid approaches and learning-based approaches that utilize traditional text representation with distributed word representation outperform the other approaches on benchmark corpora. This paper also identifies the sets of features that lead to the best-performing approaches; highlights the impacts of simple NLP tasks, such as part-of-speech tagging and parsing, on the performances of these approaches; and indicates some open problems.

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Notes

  1. http://people.rc.rit.edu/~coagla/affectdata/index.html.

  2. http://www.affective-sciences.org/index.php/download_file/view/395/296.

  3. http://web.eecs.umich.edu/~mihalcea/affectivetext/#resources.

  4. https://competitions.codalab.org/competitions/17751.

  5. https://www.linkedin.com/groups/12133338/.

  6. https://wordnet.princeton.edu.

  7. http://wndomains.fbk.eu/wnaffect.html.

  8. http://sentiwordnet.isti.cnr.it.

  9. http://sentic.net.

  10. http://mpqa.cs.pitt.edu/lexicons/subj_lexicon/.

  11. https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#lexicon.

  12. https://github.com/fnielsen/afinn.

  13. http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm.

  14. http://www.saifmohammad.com/WebPages/AffectIntensity.htm.

  15. http://saifmohammad.com/WebPages/nrc-vad.html.

  16. http://saifmohammad.com/WebPages/lexicons.html#HashEmo.

  17. http://saifmohammad.com/WebPages/lexicons.html#NRCTwitter.

  18. http://saifmohammad.com/WebPages/lexicons.html#NRCTwitter.

  19. https://affectivetweets.cms.waikato.ac.nz.

  20. https://www.cs.waikato.ac.nz/~ml/weka/.

  21. https://www.cs.waikato.ac.nz/ml/sa/lex.html#emolextwitter.

  22. http://sentistrength.wlv.ac.uk/.

  23. http://protege.stanford.edu.

  24. https://code.google.com/archive/p/word2vec.

  25. http://www.nltk.org/api/nltk.tokenize.html.

  26. https://www.nltk.org/_modules/nltk/stem/snowball.html.

  27. http://tcci.ccf.org.cn/conference/2014/pages/page04_eva.html.

  28. github.com/cbaziotis/ekphrasis.

  29. https://github.com/minimaxir/char-embeddings.

  30. http://www.wjh.harvard.edu/~inquirer/Home.html.

  31. https://translate.google.com.

  32. http://www.kamus.net.

  33. http://kbbi.web.id.

  34. http://nlp.stanford.edu/data/glove.840B.300d.zip.

  35. https://stanfordnlp.github.io/CoreNLP.

  36. http://www.cs.cmu.edu/~ark/TweetNLP.

  37. https://radimrehurek.com/gensim/index.html.

  38. https://github.com/bakrianoo/aravec.

  39. https://fasttext.cc/docs/en/crawl-vectors.html.

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Acknowledgements

This work was supported by the Research Center of the College of Computer and Information Sciences, King Saud University. We are grateful for this support. We also would like to thank the anonymous reviewers for their valuable and insightful comments.

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    Nourah Alswaidan & Mohamed El Bachir Menai

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Alswaidan, N., Menai, M.E.B. A survey of state-of-the-art approaches for emotion recognition in text. Knowl Inf Syst 62, 2937–2987 (2020). https://doi.org/10.1007/s10115-020-01449-0

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