MELD dataset format [17].

Recognizing the real emotion of humans is considered the most essential task for any customer feedback or medical applications. There are many methods available to recognize the type of emotion from speech signal by extracting frequency, pitch, and other dominant features. These features are used to train various models to auto-detect various human emotions. We cannot completely rely on the features of speech signals to detect the emotion, for instance, a customer is angry but still, he is speaking at a low voice (frequency components) which will eventually lead to wrong predictions. Even a video-based emotion detection system can be fooled by false facial expressions for various emotions. To rectify this issue, we need to make a parallel model that will train on textual data and make predictions based on the words present in the text. The model will then classify the type of emotions using more comprehensive information, thus making it a more robust model. To address this issue, we have tested four text-based classification models to classify the emotions of a customer. We examined the text-based models and compared their results which showed that the modified Encoder decoder model with attention mechanism trained on textual data achieved an accuracy of 93.5%. This research highlights the pressing need for more robust emotion recognition systems and underscores the potential of transfer models with attention mechanisms to significantly improve feedback management processes and the medical applications.精准识别人类真实情绪，是客户反馈与医疗应用领域中至关核心的任务。当前已有诸多方法可通过提取频率、基音及其他关键特征，从语音信号中识别情绪类别，此类特征可用于训练各类模型，实现人类多种情绪的自动检测。但我们无法完全依赖语音信号特征完成情绪检测：例如，即便客户处于愤怒情绪，仍可能以较低音量发声（对应频率分量特征），这将最终导致预测结果出错。即便基于视频的情绪检测系统，也可能被虚假的面部表情误导，从而误判情绪类别。为解决这一问题，我们需要构建并行模型：该模型基于文本数据进行训练，并依据文本中的词汇完成预测，通过融合更全面的信息实现情绪类别分类，进而打造出鲁棒性更强的模型。为解决上述问题，我们针对客户情绪分类任务，测试了四款基于文本的分类模型。通过对这些文本分类模型进行评估并对比其实验结果，我们发现：基于文本数据训练的改进型带注意力机制编码器-解码器模型（Encoder-decoder model with attention mechanism）准确率可达93.5%。本研究凸显了对更高鲁棒性情绪识别系统的迫切需求，同时证实了带注意力机制的迁移模型能够显著优化反馈管理流程，并赋能医疗相关应用场景。