Published Paper Details:

In today's digital communication landscape, efficient analysis of phone call conversations is imperative for various applications, including customer service enhancement and market research. This project proposes a novel approach leveraging Natural Language Processing (NLP) techniques to identify conversation threads in phone calls. The methodology encompasses preprocessing audio, converting it to text, and employing NLP algorithms such as summarization, topic modelling, and sentiment analysis for comprehensive analysis. Key components include implementing a robust speech-to-text conversion system using deep learning models fine-tuned on phone call data, followed by NLP analysis to parse and analyze transcribed text for pattern identification. Thread identification algorithms are developed based on semantic coherence and contextual cues, facilitating the segmentation of conversations into coherent threads. An intuitive user interface is designed to visualize and interact with identified conversation threads efficiently. The system's accuracy, scalability, and real-world applicability are evaluated rigorously across diverse datasets, with continual optimization to enhance performance. Evaluation metrics include precision, recall, and F1-score, providing insights into the system's effectiveness in identifying conversation topics and patterns.