## 5 Experimental Results and Analysis
### Case Study  
As shown in Table 5, the utterance of \"Uh-huh\". is too short, with little semantic information and a lack of emotional words. It is easy to classify as \"neutral\" by directly \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model**} & **IEMOCAP** & **DailyDialog** & **MELD** & **EmoryNLP** \\ \cline{2-5}  & **Weighted-Avg-F1** & **Micro-F1** & **Weighted-Avg-F1** & **Weighted-Avg-F1** \\ \hline Ours & **70.92** & **57.07** & **66.13** & **40.97** \\ -syntax & 69.02 & 55.14 & 64.98 & 39.75 \\ -topic & 67.10 & 55.89 & 65.16 & 37.52 \\ -syntax, topic & 66.63 & 54.93 & 64.04 & 37.01 \\ -dialogue graph & 61.81 & 55.18 & 63.37 & 35.59 \\ \(BERT_{base}\) & 59.45 & 53.12 & 56.21 & 33.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of ablation study on four datasets. classifying the emotion of this sentence. However, when this sentence appears in different scenes, the emotion it expresses is not the same due to the difference in its contextual sentence and speaker identities. Case one is a conversation in which [PERSON] is talking to himself, so the second sentence continues the emotion of the previous sentence. Utterance 07 \"Uh-huh\". of Case 2 is influenced by the previous discourse, and the speaker's self-influence is very important, so the emotion it predicts is correct. In case 3, the semantics of utterance 07 \"That's great\". contain positive emotional information, and the above speaker's emotion is positive, so it is detected as a positive emotion. However, in fact, its real semantics in the original text means that the commodity is very expensive, and it has the meaning of irony. Our model has not made special modifications in irony detection, which is an area that needs to be improved in the future. Case 4 is an excerpt from a long conversation. In the beginning, [PERSON] and [PERSON] are talking to each other. In utterance 4, [PERSON] answers [PERSON]'s question, but utterance 5 is issued by the new speaker [PERSON], who teases [PERSON], so the mood is affected by the current topic and is very happy. Utterance 6 is still discussing the current topic, but its semantics have a greater impact on emotion, so we can correctly distinguish its emotion, which shows that the attention mechanism that integrates syntax and topic modules has worked. Utterances 7 to 9 are greetings, with no emotional change, but two new speakers joined the conversation. Utterance 10 started to discuss a new topic. [PERSON] asked [PERSON] why [PERSON] didn't come, and then [PERSON] answered [PERSON]'s question. Utterances 10 to 12 were all conversations between [PERSON] and [PERSON], but [PERSON] praised [PERSON]'s boyfriend in utterance 13, so the interactive subject of utterance 14 [PERSON] changes from [PERSON] to [PERSON], but utterance 15 turns back to the dialogue with [PERSON]. The overall dialogue logic is very complex, and the chat topic and interaction subjects have changed many times, but our model still accurately classifies the correct emotional labels. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Num** & **ID** & **Speaker** & **Text** & **Emotion** & **Prediction** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{08} & \multirow{3}{*}{[PERSON]} & I know...watch [PERSON] & \multirow{3}{*}{Joyful} & \multirow{3}{*}{Joyful} \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline
2 & 03 & [PERSON] & Get out! & Mad & Mad \\  &... & 07 & [PERSON] & Uh-huh. & Mad & Mad \\ \hline
3 & 03 & [PERSON] & Excuse me, that’s 50 & \multirow{3}{*}{Mad} & \multirow{3}{*}{Mad} \\  &... & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline
4 & 02 & [PERSON] & Hey. & Neutral & Neutral \\  & 03 & [PERSON] & [PERSON], whatcha got & Neutral & Neutral \\  & & & & there? & \\  & & & & Ok, Love Story, [PERSON]’s & \\  & & & & & \\  & & & & & \\  & & & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table}