A Comparison of Machine Learning Techniques for Sentiment Analysis

The availability of the data has increased tremendously due to excess usage social media platforms like Twitter and Facebook. Due abundant data, scientists, businesses, educationalists other people working under different roles have started using Sentiment Analysis (SA) get in-depth knowledge about sentiments regarding any topic interest. There are many techniques implement SA, one them is Machine Learning (ML). This study focused on comparison ancient ML methods such as Naïve Bayes (NB), Decision Tree (DT), Support Vector (SVM), a modern method, i.e., Deep (DL). applied single dataset compare their performance in terms accuracy understand how they perform against each other. found that DL performed best with 96.41% followed by NB SVM 87.18% 82.05% respectively. DT poorest 68.21% accuracy.