# A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions

arXiv: https://arxiv.org/abs/2205.03235 · HTML: https://mlsanity.com/paper/2205.03235/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 170 references that are in the map.
- First posted on arXiv 26 Apr 2022.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [Distilling Task-Specific Knowledge from BERT into Simple Neural Networks](https://mlsanity.com/paper/1903.12136/) (2019)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Conversational Recommender System](https://mlsanity.com/paper/1806.03277/) (2018)
- [Artificial Intelligence in the Creative Industries: A Review](https://mlsanity.com/paper/2007.12391/) (2020)
- [Meta-learning for Few-shot Natural Language Processing: A Survey](https://mlsanity.com/paper/2007.09604/) (2020)
- [Description Based Text Classification with Reinforcement Learning](https://mlsanity.com/paper/2002.03067/) (2020)
- [Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifiers](https://mlsanity.com/paper/2103.14453/) (2021)
- [Dual Adversarial Co-Learning for Multi-Domain Text Classification](https://mlsanity.com/paper/1909.08203/) (2019)
- [Multi-Label Few-Shot Learning for Aspect Category Detection](https://mlsanity.com/paper/2105.14174/) (2021)
- [Emotion Detection in Text: a Review](https://mlsanity.com/paper/1806.00674/) (2018)
- [Exploring Transformers in Emotion Recognition: a comparison of BERT, DistillBERT, RoBERTa, XLNet and ELECTRA](https://mlsanity.com/paper/2104.02041/) (2021)
- [Distant supervision for emotion detection using Facebook reactions](https://mlsanity.com/paper/1611.02988/) (2016)
- [Enhanced Aspect-Based Sentiment Analysis Models with Progressive Self-supervised Attention Learning](https://mlsanity.com/paper/2103.03446/) (2021)
- [GANs for Semi-Supervised Opinion Spam Detection](https://mlsanity.com/paper/1903.08289/) (2019)
- [Gated Recurrent Neural Network Approach for Multilabel Emotion Detection in Microblogs](https://mlsanity.com/paper/1907.07653/) (2019)
- [Adversarial Machine Learning in Text Analysis and Generation](https://mlsanity.com/paper/2101.08675/) (2021)
- [STEP-EZ: Syntax Tree guided semantic ExPlanation for Explainable Zero-shot modeling of clinical depression symptoms from text](https://mlsanity.com/paper/2106.10928/) (2021)

## Built on by (most cited first, 6 of 6)

- [Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions](https://mlsanity.com/paper/2403.01222/) (2024)
- [Emotion4MIDI: a Lyrics-based Emotion-Labeled Symbolic Music Dataset](https://mlsanity.com/paper/2307.14783/) (2023)
- [The Application of Affective Measures in Text-based Emotion Aware Recommender Systems](https://mlsanity.com/paper/2305.04796/) (2023)
- [Level of agreement between emotions generated by Artificial Intelligence and human evaluation: a methodological proposal](https://mlsanity.com/paper/2410.08332/) (2024)
- [Predicting Stock Price Movement with LLM-Enhanced Tweet Emotion Analysis](https://mlsanity.com/paper/2510.03633/) (2025)
- [Evaluating multimodal emotion recognition in proactive conversational agents: A user study](https://mlsanity.com/paper/2605.20200/) (2026)

## Abstract (arXiv)

Artificial Intelligence (AI) has been used for processing data to make decisions, interact with humans, and understand their feelings and emotions. With the advent of the internet, people share and express their thoughts on day-to-day activities and global and local events through text messaging applications. Hence, it is essential for machines to understand emotions in opinions, feedback, and textual dialogues to provide emotionally aware responses to users in today's online world. The field of text-based emotion detection (TBED) is advancing to provide automated solutions to various applications, such as businesses, and finances, to name a few. TBED has gained a lot of attention in recent times. The paper presents a systematic literature review of the existing literature published between 2005 to 2021 in TBED. This review has meticulously examined 63 research papers from IEEE, Science Direct, Scopus, and Web of Science databases to address four primary research questions. It also reviews the different applications of TBED across various research domains and highlights its use. An overview of various emotion models, techniques, feature extraction methods, datasets, and research challenges with future directions has also been represented.