# A Deep Neural Framework for Contextual Affect Detection

arXiv: https://arxiv.org/abs/2001.10169 · HTML: https://mlsanity.com/paper/2001.10169/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [Deep Emotion Recognition in Textual Conversations: A Survey](https://mlsanity.com/paper/2211.09172/) (2022)
- [Words to Waves: Emotion-Adaptive Music Recommendation System](https://mlsanity.com/paper/2510.21724/) (2025)

## Abstract (arXiv)

A short and simple text carrying no emotion can represent some strong emotions when reading along with its context, i.e., the same sentence can express extreme anger as well as happiness depending on its context. In this paper, we propose a Contextual Affect Detection (CAD) framework which learns the inter-dependence of words in a sentence, and at the same time the inter-dependence of sentences in a dialogue. Our proposed CAD framework is based on a Gated Recurrent Unit (GRU), which is further assisted by contextual word embeddings and other diverse hand-crafted feature sets. Evaluation and analysis suggest that our model outperforms the state-of-the-art methods by 5.49% and 9.14% on Friends and EmotionPush dataset, respectively.