Sentiment Analysis

This project utilizes Python and machine learning, by employing the Textblob algorithm to assess sentiment in Amazon reviews. The objective is to generate polarity scores categorizing reviews as positive, negative, or neutral. The accuracy is gauged by aligning these scores with actual ratings (1-5) in the dataset, designating 1 and 2 as negative, 3 as neutral, and 4-5 as positive reviews. This technical solution streamlines sentiment analysis, offering a concise yet comprehensive evaluation of customer feedback.

In any organization, collecting feedback from customers about products or services is crucial. However, when feedback is in the form of text or qualitative data, traditional data analysis tools can become a bottleneck, demanding extensive time and resources for manual review. Today, organizations can efficiently overcome this challenge by employing sentiment analysis algorithms to analyze large volumes of text data, extracting valuable insights with ease.