Customer review sentiment analysis using SVC, KNN and Vader’s sentiment analysis

This research explores the application of Natural Language Processing (NLP) for analyzing customer review sentiment analysis, where NLP methods aim to extract valuable insights from text data. The process involves gathering and preparing the data, identifying key features, and training a sentimental analysis model for accurate classification. This model forms the backbone of a user-friendly frontend interface, designed for seamless interaction with the analysis result. Users can input text or review documents, which are then processed through the trained model to generate precise sentiment scores. To enhance understanding, visualizations such as charts, graphs, and word clouds are incorporated. These visuals help users to see how sentiments are changing over time and what the main topics or feelings in the data are. Ultimately, this integration approach enables businesses to use sentiment analysis to make smarter decisions and make customers happier. By understanding, how customers feel more deeply, companies can adjust their plans to better meet their needs, resulting in happier customers and better business results.