A Comparative Study of Five Deep Learning Models for Nuanced Sentiment Analysis in Customer Interactions and Integration of Top Performers into Web Application for real-time emotion analysis and detection

Abstract

In present digital age, interpreting and analysing sentiments in real-time has become increasingly vital for businesses to enhance customer satisfaction and engagement. The motive of this study focuses on the development and assessment of a system for detecting emotions in real-time, targeting analysis of customer interactions. The primary objective is to explore the efficacy of five distinct deep learning models for nuanced sentiment analysis using a diverse dataset comprising multiple texts.

To achieve this objective, we employ a comparative analysis approach, leveraging five state-of-the-art deep learning models: Hierarchical Attention Networks (HANs), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and BERT (Bidirectional Encoder Representations from Transformers). These models undergo training and assessment utilizing a comprehensive dataset comprising with customers interactions from multiple sources, for example: social media comments, chat transcripts, and product reviews, to mention but a few.

Furthermore, to facilitate practical implementation, we aim to develop a web application that seamlessly integrates the best-performing deep learning models for real-time emotion detection. The application provides users with an intuitive interface to input text and instantly receive emotion predictions, thus enabling businesses to have valuable discernments into customer sentiments and tailor their responses accordingly.

In summary, this research endeavour represents a concerted effort to unravel the complexities of real-time emotion detection in customer interactions. By interrogating the nuances of five distinct deep learning models and translating my findings into a practical web application, we aim to help businesses with the tools and acumens more important to navigate the evolving landscape of customer sentiment, cultivate significant connections with their audience, and propel enduring growth and triumph in the digital age.