1 Introduction  
1.1 Survey Contributions  
1.2 Survey Methodology  
1.3 Outline  
2 ERC Challenges and Opportunities  
2.1 Context, Speaker and Emotion Dynamics Modelling  
2.2 Common Sense, Informal Language and Sarcasm  
2.3 Real-Time ERC  
2.4 Recognizing Emotion Causes  
2.5 Different Taxonomies  
2.6 Multilingual ERC  
2.7 Interpretability  
3 ERC Taxonomies and Benchmark Datasets  
3.1 Emotion Taxonomies  
3.2 ERC Benchmark Datasets  
4 Deep Learning for Emotion Recognition in Conversations  
4.1 Convolutional Neural Networks for Feature Extraction  
4.2 The Multi Layer Perceptron  
4.3 Elman Recurrent Neural Network  
4.4 Long Short-Term Memory Networks  
4.5 Gated Recurrent Units  
4.6 Attention Mechanisms  
4.7 Memory Networks  
4.8 Graph Neural Networks  
4.9 Transformer  
5 Advisable ERC Practices  
5.1 Learning Subjectivity  
5.2 Dealing with Unbalanced Data  
6 Systematic Review Tables of ERC Works  
6.1 Benchmark of ERC Works  
7 ERC Challenges Revisited  
8 Conclusion