# Emotion-Oriented Behavior Model Using Deep Learning

arXiv: https://arxiv.org/abs/2311.14674 · HTML: https://mlsanity.com/paper/2311.14674/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 28 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1510.03820/) (2015)
- [WASSA-2017 Shared Task on Emotion Intensity](https://mlsanity.com/paper/1708.03700/) (2017)
- [Subclass Distillation](https://mlsanity.com/paper/2002.03936/) (2020)
- [A Cognitive Architecture for the Implementation of Emotions in Computing Systems](https://mlsanity.com/paper/1606.02899/) (2016)
- [Convolutional Neural Network: Text Classification Model for Open Domain Question Answering System](https://mlsanity.com/paper/1809.02479/) (2018)

## Built on by (most cited first, 1 of 1)

- [Leveraging Language Models for Emotion and Behavior Analysis in Education](https://mlsanity.com/paper/2408.06874/) (2024)

## Abstract (arXiv)

Emotions, as a fundamental ingredient of any social interaction, lead to behaviors that represent the effectiveness of the interaction through facial expressions and gestures in humans. Hence an agent must possess the social and cognitive abilities to understand human social parameters and behave accordingly. However, no such emotion-oriented behavior model is presented yet in the existing research. The emotion prediction may generate appropriate agents' behaviors for effective interaction using conversation modality. Considering the importance of emotions, and behaviors, for an agent's social interaction, an Emotion-based Behavior model is presented in this paper for Socio-cognitive artificial agents. The proposed model is implemented using tweets data trained on multiple models like Long Short-Term Memory (LSTM), Convolution Neural Network (CNN) and Bidirectional Encoder Representations from Transformers (BERT) for emotion prediction with an average accuracy of 92%, and 55% respectively. Further, using emotion predictions from CNN-LSTM, the behavior module responds using facial expressions and gestures using Behavioral Markup Language (BML). The accuracy of emotion-based behavior predictions is statistically validated using the 2-tailed Pearson correlation on the data collected from human users through questionnaires. Analysis shows that all emotion-based behaviors accurately depict human-like gestures and facial expressions based on the significant correlation at the 0.01 and 0.05 levels. This study is a steppingstone to a multi-faceted artificial agent interaction based on emotion-oriented behaviors. Cognition has significance regarding social interaction among humans.