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Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions (2403.01222v2)

Published 2 Mar 2024 in cs.CL

Abstract: Emotions are a central aspect of communication. Consequently, emotion analysis (EA) is a rapidly growing field in NLP. However, there is no consensus on scope, direction, or methods. In this paper, we conduct a thorough review of 154 relevant NLP publications from the last decade. Based on this review, we address four different questions: (1) How are EA tasks defined in NLP? (2) What are the most prominent emotion frameworks and which emotions are modeled? (3) Is the subjectivity of emotions considered in terms of demographics and cultural factors? and (4) What are the primary NLP applications for EA? We take stock of trends in EA and tasks, emotion frameworks used, existing datasets, methods, and applications. We then discuss four lacunae: (1) the absence of demographic and cultural aspects does not account for the variation in how emotions are perceived, but instead assumes they are universally experienced in the same manner; (2) the poor fit of emotion categories from the two main emotion theories to the task; (3) the lack of standardized EA terminology hinders gap identification, comparison, and future goals; and (4) the absence of interdisciplinary research isolates EA from insights in other fields. Our work will enable more focused research into EA and a more holistic approach to modeling emotions in NLP.

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References (94)
  1. Text-based emotion detection: Advances, challenges, and opportunities. Engineering Reports, 2(7):e12189.
  2. Hassan Alhuzali and Sophia Ananiadou. 2021. SpanEmo: Casting multi-label emotion classification as span-prediction. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1573–1584, Online. Association for Computational Linguistics.
  3. Uncovering values: Detecting latent moral content from natural language with explainable and non-trained methods. In Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pages 33–41, Dublin, Ireland and Online. Association for Computational Linguistics.
  4. Multimodal language analysis in the wild: CMU-MOSEI dataset and interpretable dynamic fusion graph. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2236–2246, Melbourne, Australia. Association for Computational Linguistics.
  5. Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis. Association for Computational Linguistics, Dublin, Ireland.
  6. Lisa Feldman Barrett. 2017. How emotions are made: The secret life of the brain. Pan Macmillan.
  7. We need to consider disagreement in evaluation. In Proceedings of the 1st Workshop on Benchmarking: Past, Present and Future, pages 15–21, Online. Association for Computational Linguistics.
  8. Emily M. Bender and Batya Friedman. 2018. Data statements for natural language processing: Toward mitigating system bias and enabling better science. Transactions of the Association for Computational Linguistics, 6:587–604.
  9. GoodNewsEveryone: A corpus of news headlines annotated with emotions, semantic roles, and reader perception. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1554–1566, Marseille, France. European Language Resources Association.
  10. Michael Brady. 2018. Emotion: The Basics. Routledge.
  11. A time series analysis of emotional loading in central bank statements. In Proceedings of the Second Workshop on Economics and Natural Language Processing, pages 16–21, Hong Kong. Association for Computational Linguistics.
  12. IEMOCAP: Interactive emotional dyadic motion capture database. Language resources and evaluation, 42:335–359.
  13. Sue Campbell. 1994. Being dismissed: The politics of emotional expression. Hypatia, 9(3):46–65.
  14. Lea Canales and Patricio Martínez-Barco. 2014. Emotion detection from text: A survey. In Proceedings of the Workshop on Natural Language Processing in the 5th Information Systems Research Working Days (JISIC), pages 37–43, Quito, Ecuador. Association for Computational Linguistics.
  15. SemEval-2019 task 3: EmoContext contextual emotion detection in text. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 39–48, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
  16. All-in-one: A deep attentive multi-task learning framework for humour, sarcasm, offensive, motivation, and sentiment on memes. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 281–290, Suzhou, China. Association for Computational Linguistics.
  17. Sentiment and emotion help sarcasm? a multi-task learning framework for multi-modal sarcasm, sentiment and emotion analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4351–4360, Online. Association for Computational Linguistics.
  18. RED v2: Enhancing RED dataset for multi-label emotion detection. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1392–1399, Marseille, France. European Language Resources Association.
  19. Emotion recognition based on psychological components in guided narratives for emotion regulation. In Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 72–81, Dubrovnik, Croatia. Association for Computational Linguistics.
  20. Alba Curry and Amanda Cercas Curry. 2022. Computer says" no": The case against empathetic conversational ai. arXiv preprint arXiv:2212.10983.
  21. Dealing with disagreements: Looking beyond the majority vote in subjective annotations. Transactions of the Association for Computational Linguistics, 10:92–110.
  22. Luna De Bruyne. 2023. The paradox of multilingual emotion detection. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 458–466, Toronto, Canada. Association for Computational Linguistics.
  23. Paul Ekman and Wallace V. Friesen. 1981. The Repertoire of Nonverbal Behavior: Categories, Origins, Usage, and Coding, pages 57–106. De Gruyter Mouton, Berlin, Boston.
  24. Emotion detection: A technology review. In Proceedings of the XVIII International Conference on Human Computer Interaction, Interacción ’17, New York, NY, USA. Association for Computing Machinery.
  25. COSMIC: COmmonSense knowledge for eMotion identification in conversations. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2470–2481, Online. Association for Computational Linguistics.
  26. COMMA-DEER: COmmon-sense aware multimodal multitask approach for detection of emotion and emotional reasoning in conversations. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6978–6990, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
  27. Emotion detection in email customer care. In Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pages 10–16, Los Angeles, CA. Association for Computational Linguistics.
  28. PO-EMO: Conceptualization, annotation, and modeling of aesthetic emotions in German and English poetry. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1652–1663, Marseille, France. European Language Resources Association.
  29. ICON: Interactive conversational memory network for multimodal emotion detection. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2594–2604, Brussels, Belgium. Association for Computational Linguistics.
  30. Appraisal theories for emotion classification in text. In Proceedings of the 28th International Conference on Computational Linguistics, pages 125–138, Barcelona, Spain (Online). International Committee on Computational Linguistics.
  31. DialogueCRN: Contextual reasoning networks for emotion recognition in conversations. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 7042–7052, Online. Association for Computational Linguistics.
  32. Tatsuya Ide and Daisuke Kawahara. 2022. Building a dialogue corpus annotated with expressed and experienced emotions. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 21–30, Dublin, Ireland. Association for Computational Linguistics.
  33. MuSE: a multimodal dataset of stressed emotion. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1499–1510, Marseille, France. European Language Resources Association.
  34. Bettina Judd. 2019. Sapphire as praxis: Toward a methodology of anger. Feminist Studies, 45(1):178–208.
  35. WRIME: A new dataset for emotional intensity estimation with subjective and objective annotations. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2095–2104, Online. Association for Computational Linguistics.
  36. Hamed Khanpour and Cornelia Caragea. 2018. Fine-grained emotion detection in health-related online posts. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1160–1166, Brussels, Belgium. Association for Computational Linguistics.
  37. Evgeny Kim and Roman Klinger. 2018a. A survey on sentiment and emotion analysis for computational literary studies. arXiv preprint arXiv:1808.03137.
  38. Evgeny Kim and Roman Klinger. 2018b. Who feels what and why? annotation of a literature corpus with semantic roles of emotions. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1345–1359, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
  39. IEST: WASSA-2018 implicit emotions shared task. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 31–42, Brussels, Belgium. Association for Computational Linguistics.
  40. A review on text-based emotion detection–techniques, applications, datasets, and future directions. arXiv preprint arXiv:2205.03235.
  41. A review on text-based emotion detection - techniques, applications, datasets, and future directions. ArXiv, abs/2205.03235.
  42. Universal joy a data set and results for classifying emotions across languages. In Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 62–75, Online. Association for Computational Linguistics.
  43. Richard S Lazarus. 1991. Progress on a cognitive-motivational-relational theory of emotion. American psychologist, 46(8):819.
  44. Joosung Lee and Wooin Lee. 2022. CoMPM: Context modeling with speaker’s pre-trained memory tracking for emotion recognition in conversation. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5669–5679, Seattle, United States. Association for Computational Linguistics.
  45. DailyDialog: A manually labelled multi-turn dialogue dataset. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 986–995, Taipei, Taiwan. Asian Federation of Natural Language Processing.
  46. DENS: A dataset for multi-class emotion analysis. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6293–6298, Hong Kong, China. Association for Computational Linguistics.
  47. DialogueEIN: Emotion interaction network for dialogue affective analysis. In Proceedings of the 29th International Conference on Computational Linguistics, pages 684–693, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
  48. SwahBERT: Language model of Swahili. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 303–313, Seattle, United States. Association for Computational Linguistics.
  49. Swahbert: Language model of swahili. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 303–313.
  50. Sentiment and emotion based representations for fake reviews detection. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 750–757, Varna, Bulgaria. INCOMA Ltd.
  51. SemEval-2018 task 1: Affect in tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 1–17, New Orleans, Louisiana. Association for Computational Linguistics.
  52. Saif M. Mohammad. 2021. Chapter 11 - sentiment analysis: Automatically detecting valence, emotions, and other affectual states from text. In Herbert L. Meiselman, editor, Emotion Measurement (Second Edition), second edition edition, pages 323–379. Woodhead Publishing.
  53. Saif M. Mohammad. 2022. Ethics sheet for automatic emotion recognition and sentiment analysis. Computational Linguistics, 48(2):239–278.
  54. Are they different? affect, feeling, emotion, sentiment, and opinion detection in text. IEEE Transactions on Affective Computing, 5(2):101–111.
  55. Ashritha R Murthy and KM Anil Kumar. 2021. A review of different approaches for detecting emotion from text. In IOP Conference Series: Materials Science and Engineering, volume 1110, page 012009. IOP Publishing.
  56. The evolution of sentiment analysis—a review of research topics, venues, and top cited papers. Computer Science Review, 27:16–32.
  57. I miss you babe: Analyzing emotion dynamics during COVID-19 pandemic. In Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, pages 41–49, Online. Association for Computational Linguistics.
  58. StudEmo: A non-aggregated review dataset for personalized emotion recognition. In Proceedings of the 1st Workshop on Perspectivist Approaches to NLP @LREC2022, pages 46–55, Marseille, France. European Language Resources Association.
  59. Proceedings of the Third Workshop on Computational Modeling of People’s Opinions, Personality, and Emotion’s in Social Media. Association for Computational Linguistics, Barcelona, Spain (Online).
  60. Emily Öhman. 2020. Emotion annotation: Rethinking emotion categorization. In DHN post-proceedings, pages 134–144.
  61. The ecological fallacy in annotation: Modelling human label variation goes beyond sociodemographics.
  62. The cognitive structure of emotion. Contemporary Sociology, 18.
  63. Dimensional emotion detection from categorical emotion. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4367–4380, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
  64. Rosalind W Picard. 2000. Affective computing. MIT press.
  65. Barbara Plank. 2022. The “problem” of human label variation: On ground truth in data, modeling and evaluation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10671–10682, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
  66. Natural language inference prompts for zero-shot emotion classification in text across corpora. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6805–6817, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
  67. EmoEvent: A multilingual emotion corpus based on different events. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1492–1498, Marseille, France. European Language Resources Association.
  68. Robert Plutchik. 1982. A psychoevolutionary theory of emotions. Social Science Information, 21(4-5):529–553.
  69. MELD: A multimodal multi-party dataset for emotion recognition in conversations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 527–536, Florence, Italy. Association for Computational Linguistics.
  70. Ira J Roseman. 1991. Appraisal determinants of discrete emotions. Cognition & Emotion, 5(3):161–200.
  71. Two contrasting data annotation paradigms for subjective NLP tasks. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 175–190, Seattle, United States. Association for Computational Linguistics.
  72. James A Russell and Albert Mehrabian. 1977. Evidence for a three-factor theory of emotions. Journal of Research in Personality, 11(3):273–294.
  73. Emotion detection from text and speech: a survey. Soc. Netw. Anal. Min., 8(1):28:1–28:26.
  74. Findings of the shared task on emotion analysis in Tamil. In Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages, pages 279–285, Dublin, Ireland. Association for Computational Linguistics.
  75. Emotion recognition and detection methods: A comprehensive survey. Journal of Artificial Intelligence and Systems, 2(1):53–79.
  76. Andrea Scarantino and Ronald de Sousa. 2021. Emotion. In Edward N. Zalta, editor, The Stanford Encyclopedia of Philosophy, Summer 2021 edition. Metaphysics Research Lab, Stanford University.
  77. Klaus R. Scherer. 1999. Appraisal Theory, chapter 30. John Wiley & Sons, Ltd.
  78. A sentiment and emotion annotated dataset for bitcoin price forecasting based on Reddit posts. In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP), pages 203–210, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
  79. Unimodal approaches for emotion recognition: A systematic review. Cognitive Systems Research, 77:94–109.
  80. Craig A Smith and Phoebe C Ellsworth. 1985. Patterns of cognitive appraisal in emotion. Journal of personality and social psychology, 48(4):813.
  81. Identifying emotions in code mixed hindi-english tweets. In Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference, pages 35–41.
  82. Emotion analysis and detection during COVID-19. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6938–6947, Marseille, France. European Language Resources Association.
  83. APPReddit: a corpus of Reddit posts annotated for appraisal. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3809–3818, Marseille, France. European Language Resources Association.
  84. Carlo Strapparava and Rada Mihalcea. 2008. Learning to identify emotions in text. In Proceedings of the 2008 ACM symposium on Applied computing, pages 1556–1560.
  85. Dimensional modeling of emotions in text with appraisal theories: Corpus creation, annotation reliability, and prediction. Computational Linguistics, 49(1):1–72.
  86. Crowdsourcing and validating event-focused emotion corpora for German and English. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4005–4011, Florence, Italy. Association for Computational Linguistics.
  87. Emotion-infused models for explainable psychological stress detection. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2895–2909, Online. Association for Computational Linguistics.
  88. Jonathan Turner. 2000. On the origins of human emotions: A sociological inquiry into the evolution of human affect. Stanford University Press.
  89. Current state of text sentiment analysis from opinion to emotion mining. ACM Computing Surveys, 50(2).
  90. Samira Zad and Mark Finlayson. 2020. Systematic evaluation of a framework for unsupervised emotion recognition for narrative text. In Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events, pages 26–37, Online. Association for Computational Linguistics.
  91. Sayyed M. Zahiri and Jinho D. Choi. 2017. Emotion detection on tv show transcripts with sequence-based convolutional neural networks. In AAAI Workshops.
  92. Why do you feel this way? summarizing triggers of emotions in social media posts. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9436–9453, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
  93. M3ED: Multi-modal multi-scene multi-label emotional dialogue database. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5699–5710, Dublin, Ireland. Association for Computational Linguistics.
  94. Knowledge-enriched transformer for emotion detection in textual conversations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 165–176, Hong Kong, China. Association for Computational Linguistics.
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Summary

  • The paper presents a comprehensive survey of 154 NLP studies, identifying current trends and pivotal gaps in emotion analysis frameworks.
  • It highlights the prevalent reliance on discrete emotion frameworks like Ekman and Plutchik, which limits the depth of emotional nuance.
  • The study advocates for integrating demographic, cultural, and interdisciplinary insights to standardize terminology and advance future research.

Emotion Analysis in NLP: An In-Depth Survey on Current Practices and Future Directions

Introduction to Emotion Analysis

Emotion Analysis (EA) is a notable area of exploration within the field of NLP, mirroring the significance and complexity of human emotions in communication. This survey meticulously reviews 154 pertinent NLP publications from the last decade, focusing on how EA tasks are defined, the dominant emotion frameworks, considerational gaps regarding the subjectivity of emotions in demographic and cultural contexts, and the principal NLP applications of EA. Our analysis sheds light on current trends, significant gaps, and provides a strategic roadmap for future research in EA.

Current Trends and Practices in EA

Our survey highlights several key aspects of current EA practices in NLP:

  • Emotion Frameworks and Modeling: The majority of EA studies leverage well-established emotion frameworks such as Ekman and Plutchik, with a strong inclination towards categorizing emotions into discrete classes. This categorization aligns with prevailing machine learning paradigms but limits the granularity and nuance of emotion understanding.
  • Datasets and Annotations: There exists an abundance of datasets, primarily centering around emotion recognition in conversations with a notable reliance on English language sources. While a rich variety of sources is utilized, ranging from social media posts to narrative texts, there is a manifest scarcity in datasets that cover a broader spectrum of emotions or consider demographic and cultural variability in emotion perception.
  • Applications of EA: The scope of EA extends across various domains including, but not limited to, dialogue systems, narrative text analysis, and mental health applications. The prominence of dialogue emotion recognition reflects the growing interest in developing empathetic and responsive AI systems.

Identified Gaps and Future Directions

This survey underscores four critical areas requiring attention and improvement in future EA research:

  • Demographic and Cultural Considerations: The absence of demographic and cultural considerations in most EA studies starkly contrasts with the inherent subjectivity and variability of human emotions. Future research must incorporate these factors to achieve a more comprehensive and nuanced understanding of emotions.
  • Diversity of Emotion Categories and Frameworks: The prevailing use of specific emotion frameworks restricts the adaptability and applicability of EA techniques. Future efforts should diversify the range of emotions studied and explore alternative frameworks that better capture the complex nature of human emotions.
  • Standardization of Terminology: The survey points out the inconsistent use of terminology within the EA domain, which could lead to confusion and hinder the progress of this research area. A standardized nomenclature tailored to distinct EA tasks could facilitate clearer communication and collaboration within the research community.
  • Interdisciplinary Research: The lacuna in interdisciplinary research isolates EA from potentially enriching insights from psychology, social sciences, and humanities. A more integrative approach could unveil novel methodologies and theoretical foundations, consequently enhancing NLP models' ability to grasp and analyze human emotions.

Implications and Speculations

The implications of addressing these gaps are extensive. Considering demographic and cultural factors can lead to the development of more inclusive and sensitive AI systems capable of understanding a wider array of human emotions. Diversifying emotion categories and frameworks could enhance the applicability of EA across different domains and tasks, from improving customer service interactions to supporting mental health assessments. Additionally, a unified nomenclature and interdisciplinary collaboration could significantly advance the field's methodological rigor and theoretical depth.

Looking forward, the integration of demographic information and the exploration of nuanced emotion categories may pave the way for personalized NLP applications. These applications could better respect and reflect the rich diversity of human emotional experiences, leading to technology that is more empathetic and culturally aware. Furthermore, the synergy between NLP and disciplines such as psychology could result in innovative models that more accurately mirror the complexity of human emotions, setting new standards for AI's emotional intelligence.

Conclusion

This survey provides a comprehensive overview of the state-of-the-art in EA within NLP, highlighting the important trends, gaps, and future directions. By addressing the identified lacunae, the NLP community can forge a path towards more nuanced, inclusive, and effective emotion analysis, fostering systems that better encapsulate the breadth of human experience. The roadmap outlined herein not only aims to prompt focused research efforts but also advocates for a holistic approach to emotion modeling, essential for the next leaps forward in AI's emotional comprehension.

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