To Study and Analyze Sentiment Analysis of Customer Reviews Using Natural Language Processing Techniques

Abstract: Customer reviews are very important in today's digital world for influencing potential customers and for establishing brand perception. A Natural Language Processing (NLP) technique called sentiment analysis makes it possible to automatically read textual opinions and identify whether they are very negative, negative, neutral, positive and very positive. This study explores the use of machine learning algorithms and a variety of natural language processing techniques for sentiment analysis of customer evaluation. Text preprocessing, vectorization, model training, and performance assessment using metrics like accuracy, precision, recall, and F1-score are all included in the study. The findings show that when it comes to understanding contextual sentiment in customer evaluations, deep learning model, particularly LSTM perform better than conventional machine learning models.

Sentiment Analysis, Customer Reviews, Natural Language Processing (NLP), Text Processing, Machine Learning, Deep Learning and LSTM

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