Instead of clearly defined rules, this sentiment analysis model uses machine learning to figure out the essence of the statement. This ensures that the exactitude of the analysis improves and information can be processed on many criteria without it being too complicated. This approach involves the use of machine learning algorithms under supervision. An algorithm is trained with many sample passages until it can predict with accuracy the sentiment of the text. Then large pieces of text are fed into the classifier and it predicts the sentiment as negative, neutral or positive. Machine learning models can be of two kinds such as Traditional Models and Deep Learning Models. The main models used for sentiment analysis classification algorithms are Naïve Bayes and Deep Learning.