CONVERASATIONAL AI FOR CUSTOMER FEEDBACK ANALYSIS

Abstract

Conversational Artificial Intelligence systems are designed to simulate human-like communication using Natural Language Processing and Machine Learning techniques. These systems enable computers to understand, interpret, and respond to human language in an intelligent and meaningful manner. This project focuses on developing a Conversational AI-based customer feedback analysis system that collects user feedback through an interactive chatbot interface and automatically analyzes customer opinions using AI technologies. Traditional feedback collection methods such as forms, surveys, and manual interviews are often time-consuming and may not encourage detailed user responses. In contrast, conversational AI systems provide a more natural and interactive communication environment where users can share their opinions, experiences, and suggestions freely. This approach improves customer engagement and helps organizations gather more authentic and detailed feedback data. The collected feedback is processed using various NLP techniques such as text preprocessing, tokenization, feature extraction, and sentiment analysis. The system classifies customer feedback into positive, negative, or neutral sentiments and identifies meaningful insights such as common customer complaints, frequently requested features, and areas requiring improvement. Machine learning models are used to analyze textual data accurately and generate real-time analytical results.