Comparative Analysis of Sentiment Classification using Feature Engineering for Social Media

The internet is a wide virtual space where people may express and share their personal thoughts, which has an impact on many aspects of life, including communication and marketing strategies. Monitoring social media activity is essential for determining client loyalty and attitude toward businesses or products since social media platforms. This study has used a variety of machine learning models for sentiment analysis such as SVM, Random Forest, Gradient Boosting, Logistic Regression and Decision Trees. This study evaluates how well these models perform in properly identifying attitudes in social media data and offer insights into how well they are effective in this regard. The study combines audience statistics from public sources and links sentiment analysis from social media with observations from an established research agency that specializes in media analysis. This study highlights the importance of social media in comprehending customer attitudes and its possible influence on marketing tactics.