Machine Learning and Sentiment Analysis: Analysing Customer Feedback

Abstract

Customer feedback shapes businesses and improves customer experiences in the age of advanced technology and interconnectedness. This study uses machine learning in sentiment analysis to gain customer feedback insights. An efficient and automated method to analyze large volumes of customer comments, reviews, and opinions will help businesses make data-driven decisions. The study begins with sentiment analysis, machine learning, and natural language processing theory. Lexicon-based, machine learning classifier, and deep learning sentiment analysis methods are compared for customer feedback data handling. Next, a large dataset of customer feedback samples from online sources, social media, and review sites is collected. Preprocessing the data handles noise, missing values, and feature extraction to make it suitable for machine learning algorithms. The experimental phase uses several cutting-edge machine learning models to analyze customer feedback sentiment. The proposed work also examines ensemble and transfer learning methods to improve model performance.