# Deep learning for affective computing: text-based emotion recognition in decision support

arXiv: https://arxiv.org/abs/1803.06397 · HTML: https://mlsanity.com/paper/1803.06397/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2018.

## Built on by (most cited first, 14 of 14)

- [Multimodal Engagement Analysis from Facial Videos in the Classroom](https://mlsanity.com/paper/2101.04215/) (2021)
- [Contrastive Domain Adaptation for Question Answering using Limited Text Corpora](https://mlsanity.com/paper/2108.13854/) (2021)
- [HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition](https://mlsanity.com/paper/2102.01909/) (2021)
- [Physics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertainty](https://mlsanity.com/paper/2110.08607/) (2021)
- [Shapes of Emotions: Multimodal Emotion Recognition in Conversations via Emotion Shifts](https://mlsanity.com/paper/2112.01938/) (2021)
- [Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels](https://mlsanity.com/paper/2101.03477/) (2021)
- [Multistage linguistic conditioning of convolutional layers for speech emotion recognition](https://mlsanity.com/paper/2110.06650/) (2021)
- [Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approach](https://mlsanity.com/paper/2507.08217/) (2025)
- [QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation](https://mlsanity.com/paper/2210.10861/) (2022)
- [Fine-tuning Strategies for Domain Specific Question Answering under Low Annotation Budget Constraints](https://mlsanity.com/paper/2401.09168/) (2024)
- [DeepEmotex: Classifying Emotion in Text Messages using Deep Transfer Learning](https://mlsanity.com/paper/2206.06775/) (2022)
- [REDAffectiveLM: Leveraging Affect Enriched Embedding and Transformer-based Neural Language Model for Readers' Emotion Detection](https://mlsanity.com/paper/2301.08995/) (2023)
- [A Joint-Reasoning based Disease Q&A System](https://mlsanity.com/paper/2401.03181/) (2024)
- [DEAP DIVE: Dataset Investigation with Vision transformers for EEG evaluation](https://mlsanity.com/paper/2510.00725/) (2025)

## Abstract (arXiv)

Emotions widely affect human decision-making. This fact is taken into account by affective computing with the goal of tailoring decision support to the emotional states of individuals. However, the accurate recognition of emotions within narrative documents presents a challenging undertaking due to the complexity and ambiguity of language. Performance improvements can be achieved through deep learning; yet, as demonstrated in this paper, the specific nature of this task requires the customization of recurrent neural networks with regard to bidirectional processing, dropout layers as a means of regularization, and weighted loss functions. In addition, we propose sent2affect, a tailored form of transfer learning for affective computing: here the network is pre-trained for a different task (i.e. sentiment analysis), while the output layer is subsequently tuned to the task of emotion recognition. The resulting performance is evaluated in a holistic setting across 6 benchmark datasets, where we find that both recurrent neural networks and transfer learning consistently outperform traditional machine learning. Altogether, the findings have considerable implications for the use of affective computing.