Sentiment Analysis and Facial Expression Recognition in Customer Service Interactions

In the evolving landscape of digital customer service, the need for advanced methods to accurately understand and respond to customer emotions has become critical. Traditional systems often rely solely on textual data, missing non-verbal cues that significantly contribute to the customer's emotional state. This study proposes a combined approach integrating Facial Expression Recognition (FER) and Natural Language Processing (NLP) to enhance emotion detection accuracy in customer service interactions. The FER component employs Convolutional Neural Networks (CNNs) to analyze facial expressions, while the NLP component uses Long Short-Term Memory (LSTM) networks to process textual data. This multimodal system aims to provide a comprehensive understanding of customer emotions by capturing both verbal and non-verbal cues. Experiments demonstrate that the integrated FER and NLP model significantly outperforms standalone models, achieving an accuracy of 92.3%, compared to 85.2% for FER-only and 87.4% for NLP-only models. The results highlight the benefits of a multimodal approach, showing substantial improvements in both training and validation performance. This study also compares the proposed model with other state-of-the-art models such as the Deep Learning Assisted Semantic Text Analysis (DLSTA) and Multimodal Emotion Recognition using Deep Belief Networks (DBN). While DLSTA achieves higher accuracy in text-based emotion detection, and DBNs provide robust emotion classification by integrating various modalities, our model effectively balances the strengths of both visual and textual data. The findings suggest that integrating FER and NLP can significantly enhance the quality of customer service by enabling more empathetic and effective interactions. Future work will focus on optimizing computational efficiency, addressing data variability, and ensuring adaptability across diverse customer service scenarios.