Abstract

The rapid expansion of social networking platforms has catalyzed a surge in usergenerated opinions on daily issues, including product and service evaluations. Understanding these sentiments is crucial for both consumers and service providers. This paper proposes a comprehensive Natural Language Processing (NLP) pipeline integrating sentiment analysis, topic modeling, and text classification to analyze and categorize user reviews effectively. The aim is to provide insights to marketers for informed decision-making. Through data collection, preprocessing, model development, testing, this system aims to decode consumer sentiments, identify emerging trends, and guide future product development.