# Emotion Detection in Text: a Review

arXiv: https://arxiv.org/abs/1806.00674 · HTML: https://mlsanity.com/paper/1806.00674/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2018.

## Built on by (most cited first, 10 of 10)

- [BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages](https://mlsanity.com/paper/2502.11926/) (2025)
- [Random-Set Neural Networks (RS-NN)](https://mlsanity.com/paper/2307.05772/) (2023)
- [Fine-Grained Emotion Prediction by Modeling Emotion Definitions](https://mlsanity.com/paper/2107.12135/) (2021)
- [A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions](https://mlsanity.com/paper/2205.03235/) (2022)
- [Lightweight Toxicity Detection in Spoken Language: A Transformer-based Approach for Edge Devices](https://mlsanity.com/paper/2304.11408/) (2023)
- [Happy Dance, Slow Clap: Using Reaction GIFs to Predict Induced Affect on Twitter](https://mlsanity.com/paper/2105.09967/) (2021)
- [AMAQA: A Metadata-based QA Dataset for RAG Systems](https://mlsanity.com/paper/2505.13557/) (2025)
- [Analysing the Greek Parliament Records with Emotion Classification](https://mlsanity.com/paper/2205.12012/) (2022)
- [Multimodal Sentiment Analysis: Perceived vs Induced Sentiments](https://mlsanity.com/paper/2312.07627/) (2023)
- [A Baseline Multimodal Approach to Emotion Recognition in Conversations](https://mlsanity.com/paper/2602.00914/) (2026)

## Abstract (arXiv)

In recent years, emotion detection in text has become more popular due to its vast potential applications in marketing, political science, psychology, human-computer interaction, artificial intelligence, etc. Access to a huge amount of textual data, especially opinionated and self-expression text also played a special role to bring attention to this field. In this paper, we review the work that has been done in identifying emotion expressions in text and argue that although many techniques, methodologies, and models have been created to detect emotion in text, there are various reasons that make these methods insufficient. Although, there is an essential need to improve the design and architecture of current systems, factors such as the complexity of human emotions, and the use of implicit and metaphorical language in expressing it, lead us to think that just re-purposing standard methodologies will not be enough to capture these complexities, and it is important to pay attention to the linguistic intricacies of emotion expression.