# Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions

arXiv: https://arxiv.org/abs/2403.01222 · HTML: https://mlsanity.com/paper/2403.01222/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 97 references that are in the map.
- First posted on arXiv 2 Mar 2024.

## Builds on (most cited first, 28 of 28)

- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations](https://mlsanity.com/paper/2110.05719/) (2021)
- [MultiVerS: Improving scientific claim verification with weak supervision and full-document context](https://mlsanity.com/paper/2112.01640/) (2021)
- [COSMIC: COmmonSense knowledge for eMotion Identification in Conversations](https://mlsanity.com/paper/2010.02795/) (2020)
- [Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks](https://mlsanity.com/paper/1708.04299/) (2017)
- [Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations](https://mlsanity.com/paper/1909.10681/) (2019)
- [DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversations](https://mlsanity.com/paper/2106.01978/) (2021)
- [M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database](https://mlsanity.com/paper/2205.10237/) (2022)
- [Dimensional Modeling of Emotions in Text with Appraisal Theories: Corpus Creation, Annotation Reliability, and Prediction](https://mlsanity.com/paper/2206.05238/) (2022)
- [The Ecological Fallacy in Annotation: Modelling Human Label Variation goes beyond Sociodemographics](https://mlsanity.com/paper/2306.11559/) (2023)
- [GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception](https://mlsanity.com/paper/1912.03184/) (2019)
- [SpanEmo: Casting Multi-label Emotion Classification as Span-prediction](https://mlsanity.com/paper/2101.10038/) (2021)
- [CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation](https://mlsanity.com/paper/2108.11626/) (2021)
- [Appraisal Theories for Emotion Classification in Text](https://mlsanity.com/paper/2003.14155/) (2020)
- [Crowdsourcing and Validating Event-focused Emotion Corpora for German and English](https://mlsanity.com/paper/1905.13618/) (2019)
- [Dimensional Emotion Detection from Categorical Emotion](https://mlsanity.com/paper/1911.02499/) (2019)
- [The Evolution of Sentiment Analysis - A Review of Research Topics, Venues, and Top Cited Papers](https://mlsanity.com/paper/1612.01556/) (2016)
- [DENS: A Dataset for Multi-class Emotion Analysis](https://mlsanity.com/paper/1910.11769/) (2019)
- [PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry](https://mlsanity.com/paper/2003.07723/) (2020)
- [A Survey on Sentiment and Emotion Analysis for Computational Literary Studies](https://mlsanity.com/paper/1808.03137/) (2018)
- [Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora](https://mlsanity.com/paper/2209.06701/) (2022)
- [IEST: WASSA-2018 Implicit Emotions Shared Task](https://mlsanity.com/paper/1809.01083/) (2018)
- [Emotion Recognition based on Psychological Components in Guided Narratives for Emotion Regulation](https://mlsanity.com/paper/2305.10446/) (2023)
- [A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions](https://mlsanity.com/paper/2205.03235/) (2022)
- [Emotion analysis and detection during COVID-19](https://mlsanity.com/paper/2107.11020/) (2021)
- [Building a Dialogue Corpus Annotated with Expressed and Experienced Emotions](https://mlsanity.com/paper/2205.11867/) (2022)
- [A Time Series Analysis of Emotional Loading in Central Bank Statements](https://mlsanity.com/paper/1911.11522/) (2019)

## Built on by (most cited first, 27 of 27)

- [Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution](https://mlsanity.com/paper/2403.03121/) (2024)
- [BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages](https://mlsanity.com/paper/2502.11926/) (2025)
- [CULEMO: Cultural Lenses on Emotion -- Benchmarking LLMs for Cross-Cultural Emotion Understanding](https://mlsanity.com/paper/2503.10688/) (2025)
- [Divine LLaMAs: Bias, Stereotypes, Stigmatization, and Emotion Representation of Religion in Large Language Models](https://mlsanity.com/paper/2407.06908/) (2024)
- [Which Demographics do LLMs Default to During Annotation?](https://mlsanity.com/paper/2410.08820/) (2024)
- [Language Models Predict Empathy Gaps Between Social In-groups and Out-groups](https://mlsanity.com/paper/2503.01030/) (2025)
- [iNews: A Multimodal Dataset for Modeling Personalized Affective Responses to News](https://mlsanity.com/paper/2503.03335/) (2025)
- [Exploratory Study into Relations between Cognitive Distortions and Emotional Appraisals](https://mlsanity.com/paper/2503.15979/) (2025)
- [ExpressivityBench: Can LLMs Communicate Implicitly?](https://mlsanity.com/paper/2411.08010/) (2024)
- [MASIVE: Open-Ended Affective State Identification in English and Spanish](https://mlsanity.com/paper/2407.12196/) (2024)
- [Once More, With Feeling: Measuring Emotion of Acting Performances in Contemporary American Film](https://mlsanity.com/paper/2411.10018/) (2024)
- [EmoGRACE: Aspect-based emotion analysis for social media data](https://mlsanity.com/paper/2503.15133/) (2025)
- [Do Language Models Agree with Human Perceptions of Suspense in Stories?](https://mlsanity.com/paper/2508.15794/) (2025)
- [Improving Interpretability of Lexical Semantic Change with Neurobiological Features](https://mlsanity.com/paper/2602.09760/) (2026)
- [Quality and Agreement in Multilabel Emotion Annotation: A Case Study and Evaluation Framework](https://mlsanity.com/paper/2606.21069/) (2026)
- [Structuring the Space of Perspectives](https://mlsanity.com/paper/2608.12113/) (2026)
- [Disambiguation of Emotion Annotations by Contextualizing Events in Plausible Narratives](https://mlsanity.com/paper/2508.09954/) (2025)
- [Exploring Subjective Tasks in Farsi: A Survey Analysis and Evaluation of Language Models](https://mlsanity.com/paper/2509.05719/) (2025)
- [Hype or not? Formalizing Automatic Promotional Language Detection in Biomedical Research](https://mlsanity.com/paper/2509.24638/) (2025)
- [A Customer Journey in the Land of Oz: Leveraging the Wizard of Oz Technique to Model Emotions in Customer Service Interactions](https://mlsanity.com/paper/2511.21909/) (2025)
- [Story2MIDI: Emotionally Aligned Music Generation from Text](https://mlsanity.com/paper/2512.02192/) (2025)
- [Detecting Emotion Drift in Mental Health Text Using Pre-Trained Transformers](https://mlsanity.com/paper/2512.13363/) (2025)
- [Towards Expectation Detection in Language: A Case Study on Treatment Expectations in Reddit](https://mlsanity.com/paper/2602.15504/) (2026)
- [Persona-E2: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events](https://mlsanity.com/paper/2604.09162/) (2026)
- [Multilingual Multi-Label Emotion Classification at Scale with Synthetic Data](https://mlsanity.com/paper/2604.12633/) (2026)
- [From Chatbots to Confidants: A Cross-Cultural Study of LLM Adoption for Emotional Support](https://mlsanity.com/paper/2604.25525/) (2026)
- [Double-Edged Sword or Sharp Tool? Designing and Evaluating Triadic LLM-Teacher Collaboration for K-12 Writing at Scale](https://mlsanity.com/paper/2605.30200/) (2026)

## Abstract (arXiv)

Emotions are a central aspect of communication. Consequently, emotion analysis (EA) is a rapidly growing field in natural language processing (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.