Sentiment Analysis of Customer Satisfaction Using ML Models

Consumers frequently share reviews that reflect their satisfaction and experiences about products. These reviews can be categorized to provide an overall understanding of consumer sentiment, offering insights for retailers to improve their products and services and for other consumers to make informed decisions. This study reviews methods used to analyze sentiment in customer feedback, focusing on traditional machine learning approaches as well as recent deep learning techniques. This study compares different sentiment analysis tools across different products. Challenges such as extracting context, handling indirect expressions of sentiment, and managing data quality are explored. This analysis highlights how sentiment analysis can improve personalized recommendations and support retailers in understanding and responding to customer needs more effectively.