## 1 Introduction  
In this society dominated by big data, sentiment analysis has gained more and more attention from researchers because of its important applications in the business field. The most extensive application of sentiment analysis lies in the screening and classification of comments [1]. Businesses understand market dynamics and improve market competitiveness by analyzing user comments. Not only that, with the continuous development of social media and the increasing number of human-computer interactions [2, 3], sentiment analysis in dialogue has also been proposed by researchers as an emerging task. Traditional emotion recognition work generally takes non-dialogical text as the research object [4, 5]. Researchers extract the sentiment statements in the text and analyze the sentiment polarity, emotions, evaluations, and opinion attitudes generated by humanstowards a target object [6; 7]. Emotion recognition in conversations is a new task proposed in recent years [8]. Unlike traditional emotion recognition, conversational texts have more than one speaker, the speakers produce emotional exchanges and collisions of opinions during the conversation, and their emotional volatility is more frequent and intense. Secondly, dialogue text is the language of daily communication, and speakers can conceal their true intentions through different expressions, so we not only need to extract the original meaning of the text but also need to explore the speaker's intention hidden in the text [9; 10]. [PERSON] et al. [11] explained three characteristics of emotion transmission in dialogue: context dependence, persistence, and contagiousness. [PERSON] et al. [12] studied the implicit semantics of dialogue texts. However, their study subjects were all one-to-one dialogue texts. In this study, our goal is to address the problem of emotion recognition in the context of conversations between three and more speakers, a situation we refer to as multi-speaker conversations. Multi-speaker conversations are characterized by having multiple speakers and are not a one-on-one speaking mode, so it is more challenging than a two-speaker conversation. First, because the emotional interaction of a conversation is a continuous process rather than a short fixed one, the emotional state of the conversation is continuously changing. The emotions of a two-speaker conversation are only influenced by each other's words, while the emotions of a multi-speaker conversation may be influenced by the words of multiple interlocutors, and its emotional determination is more complex. Second, when two people talk, the content of the conversation is more organized and logical, but when there are more participants, a conversation may have multiple topics and each speaker has different concerns and different objects. One speaker can respond to multiple speakers at the same time, which makes the interaction more complex. Finally, the number of multi-speaker conversations is generally much larger than that of two-speaker conversations, and the long-term dependencies problem arising from this is difficult to solve with existing technologies. Moreover, due to the irregularity of daily languages, the literal sentiment of discourse may be inconsistent with the actual sentiment it expresses [12], and we define this inconsistency as the difference between the original meaning and the derived meaning of an utterance, and how to explore the derived meaning of an utterance is the work we must accomplish. Inspired by graph-based models proposed in other tasks [13; 14], we propose joint syntax-enhanced and topic-driven graph networks for emotion recognition in multi-speaker conversations, namely SETD-ERC. SETD-ERC has three modules, where the syntax module analyzes the sentence components of the input utterances to obtain the dependencies between word components, using this structural information allows better mining of the latent semantic information of the sentences.