## 3 Proposed Approach  
This section details our proposed approach, outlining the data processing steps and the classification model architectures utilized. Our research aims to create a more nuanced understanding of emotional expression by integrating SER and SA. This integration allows us to introduce a novel category of complex emotions, enriching the description of affective states within each utterance. Table 1 presents the individual emotion classes for SER and SA, along with combined emotions used in our study to validate this approach. When it comes to developing an approach for emotion recognition tasks, understanding the nuances of human emotion is paramount. Emotions are not always simple or discrete; they often manifest as complex blends of different affective states. To capture this complexity, we leverage [PERSON]'s [34] arousal-valence model of affect and [PERSON]'s wheel of emotions [35], two fundamental frameworks in the study of emotions in psychology that is relevant for affective computing. [PERSON]'s model posits that emotions can be mapped onto a two-dimensional space defined by arousal (the intensity of the emotion) and valence (the pleasantness or unpleasantness of the emotion). \begin{table}
\begin{tabular}{c|c|c|c|c} \hline \hline
**SER** & **SA** & \begin{tabular}{c} **Complex Emotion** \\ **([PERSON] and [PERSON])** \\ \end{tabular}  & \begin{tabular}{c} **Theoretical Justification** \\ \end{tabular}  & \begin{tabular}{c} **Other References** \\ \end{tabular}  \\ \hline Sad & Positive & \begin{tabular}{c} **Wistful, bittersweet,** \\ **grieving** \\ \end{tabular}  & \begin{tabular}{c} **Low arousal (sad) + positive** \\ **valence (positive sentiment) =** \\ **mixed emotions, reflecting on** \\ **positive past experiences with** \\ **sadness due to their absence.** \\ \end{tabular}  & \begin{tabular}{c} **Mixed emotions are common and** \\ **have been studied extensively [22].** \\ \end{tabular}  \\ \hline Sad & Negative & \begin{tabular}{c} **Despair, hopelessness** \\ \end{tabular}  & \begin{tabular}{c} **Low arousal (sad) + negative** \\ **valence (negative sentiment) =** \\ **apathy and anhedonia,** \\ **characteristic of depression.** \\ \end{tabular}  & \begin{tabular}{c} **The reinforcement of negative** \\ **emotions is a hallmark of despair** \\ **and possibly depression [23].** \\ \end{tabular}  \\ \hline Sad & Neutral & \begin{tabular}{c} **Melancholy, pensive** \\ **perseive or negative sentiment,** \\ **associated with reflection.** \\ \end{tabular}  & \ \begin{tabular}{c} **Melancholy can be often** \\ **associated with depression and** \\ **other mood disorders [24].** \\ \end{tabular}  \\ \hline Happy & Positive &
\begin{tabular}{c} **Joyful, elated** \\ **the  
[PERSON]'s wheel, on the other hand, categorizes emotions into primary (e.g., joy, sadness, anger, fear) and secondary (e.g., love, guilt, shame) categories, also considering their intensity. In our study, we combine these frameworks to analyze complex emotions arising from the fusion of SER and SA. We consider speech emotion as a measure of arousal (angry, neutral, happy, sad, and surprise) and text sentiment as a measure of valence (positive, negative, and neutral). By mapping the different combinations of speech emotion and text sentiment onto the arousal-valence space, we can identify and label more nuanced emotional states. For example, the combination of \"sad\" speech emotion (low arousal) and \"positive\" text sentiment (positive valence) might indicate a complex emotion like \"wistful\" or \"bitersweet.\" This reflects a state where the individual is experiencing sadness but also reminiscing about positive past experiences. Similarly, the combination of \"angry\" speech emotion (high arousal) and \"positive\" text sentiment might suggest \"frustration\" or \"irritation,\" indicating a desire for change or improvement despite the anger.