## 2 Related Work
### Emotion Recognition in Conversations  
Emotion recognition in conversations has been a newly proposed task in recent years. The traditional non-dialogical texts are organized and logical with smooth sentiment changes, while the emotion of dialogic utterances is more complex as it is influenced by the context and interlocutors' emotions. [PERSON] et al. [29] proposed a search algorithm for tracking social network users. [PERSON] et al. [30] applied the technology of tracking the state of the speakers to dialogue sentiment analysis, they thought that the current speaker's state was influenced by that speaker's previous state and the preceding context. Based on this assumption, the speaker's state information is updated. On this basis, [PERSON] et al. [31] proposed the speaker's self-dependence and inter-speaker dependence, and they argued that this two relational information can explain the change of emotional information in the conversation. Speaker's self-dependence simulates the emotional inertia of individual speakers, while inter-speaker dependence simulates the interaction between speakers. [PERSON] also replaced the RNN with GCN to optimize the context propagation processing method. Graph convolutional network (GCN) is a neural network technology commonly used in dialogue sentiment analysis. [PERSON] et al. [32] use GCN to classify nodes according to their characteristics and their relationships. [PERSON] et al. [33] extracted dialogue round information, topic information, and speaker information as well as overall dialogue information. They designed a dialogue graph to simulate the interaction between these components and also combined a bidirectional long short-term memory (BiLSTM) with GCN to build up a new mechanism for processing contextual information.  
[PERSON] et al. [34] focused on the multi-speaker dialogue emotion recognition task, and they are the first ones who considered both context-sensitive and speaker-sensitive dependencies in this task. They chose to use graphs, which transform the sentiment detection task into a classification problem for graph nodes, to describe the emotional relationships between speakers. However, they did not focus on the extraction of semantic information and ignored that the topic of the conversation is easy to change. [PERSON] et al. [35] presented that the existing work was not conducive to the application of the pre-trained model XLNet [36]. They adjusted the memory update of XLNet so that it could retain more historical information and designed four attention mechanisms to model the dependencies arising from the dialogue process. However, their study did not make updates to the multi-speaker dialogue emotion recognition task. [PERSON] et al. [37] considered for the first time the importance of dialogue structure in processing information and shaping speaker characteristics, and they exploit a gated convolution to propagate contextual information by using the self-speaker dependency of interlocutors.  
The above approaches mention the factors that influence the effectiveness of emotion recognition methods such as semantics, syntactic structure, thematic information, and inter-speaker interaction, but their work only mentions one or two of the elements and does not consider them completely. Our study integrates all the influencing factors of multi-speaker dialogue emotion recognition and designs a joint syntax-enhanced and topic-driven graph network (SETD-ERC). We extract syntactic and thematic information so that the sentence representation vector retains more valid information. Unlike existing graph-based neural networks, we apply topic segmentation techniques to multi-speaker dialogue and exploit a special dialogue graph structure to model speaker identification information and handle discourse-emotion interactions between speakers.