## 2 Related Work  
Conversational sentiment analysis differs from sentence-level and chapter-level sentiment analysis in that it needs to consider the contextual background of the conversation and the affective interactions between different speakers [6,8]. Therefore, contextual relevance models and speaker relevance models are two entry points for conversational sentiment analysis. In addition, to improve the validity of the models, some scholars also assisted modeling with other information, such as sentiment congruence and conversation topic information. Figure 1: Example of customer service conversation. Contextual correlation models [9] mainly use contextual information in conversations to help the model improve sentiment analysis. [PERSON] et al. [9] proposed a cLSTM model that enables utterances to capture contextual information from their surroundings, which first trains a separate CNN model for the vector representation of all utterances in a conversation, then inputs the vector representation into the LSTM model to obtain the contextual association representation of the utterance and finally recognizes the utterance sentiment. [PERSON] et al. [10] proposed a knowledge-enriched transformer (KET) to effectively incorporate contextual information and external knowledge bases to identify sentiment, which uses self-attention and cross-attention modules to exploit contextual information more efficiently. [PERSON] et al. [11] proposed a method about target-dependent sentiment classification, which mainly uses target-dependent long short-term memory to capture contextual information and model the relatedness of a target word with its context words. [PERSON] et al. [12] utilized two independent bidirectional LSTM and GRU layers to extract both past and future contexts by combining the forward and backward hidden layers, and used the attention mechanism to focus on important parts. The speaker-related model [13] focuses on the speaker's state and interactions while considering conversational contextual information. [PERSON] et al. [13] proposed a CMN model, which stores the context information in memories and models the interplay of these memories to capture inter-speaker dependencies. The CMN model selected separate memory units for different speakers. Based on this model, [PERSON] et al. [14] proposed an ICON model, which uses a unified memory unit, realizes separate textual representations of the historical discourse of different speakers using the SIM module, then globally integrates them through the DGIM module. [PERSON] et al. [15] proposed a DialogueRNN model, which uses three GRU networks (speaker state GRU, global state GRU, and sentiment state GRU) to capture speaker information, historical discourse, and sentiment information, respectively. [PERSON] et al. [16] propose a graph convolutional neural network-based model, ConGCN, which uses each speaker and their corresponding utterance representations as nodes in the graph and associates them through undirected edges. [PERSON] et al. [17] also used graph convolutional neural networks to model conversations, but the difference is that the DialogueCN model contains only utterance nodes and indirectly associates them with previous speakers by deciding the type of edges based on different speaker information. Current research has shown that the approach of incorporating the speaker's affective interaction or contextual information is an effective means to improve the accuracy of conversational sentiment recognition. Based on this, some scholars also assisted modeling with other information, such as sentiment consistency and conversation topic information, which can help to improve the accuracy of sentiment recognition. For sentiment consistency modeling, [PERSON] et al. [18] transformed the conversation sentiment recognition task into a sequence-tagging task for sentiment consistency modeling, using a conditional random field model layer to learn the sentiment consistency in conversations. [PERSON] et al.