One of the main challenges of the NLP is emotional representation or perception mining (natural language processing). In this scenario, market analytics play a critical role in identifying that people want to expand their business. In fact, these people use inputs from goods that customers have used and based on their inputs and feedback give a clear-cut idea for the business people how to withstand in the present market and also gives an outstanding picture of what they should expect in the future. With few words or phrases, the results will be chosen. Thus, these individuals aim to boost their market by offering premium goods to their customers for attaining maximum benefit. Sentimental analysis has also gained a lot of interest in recent years. SA is an NLP analysis area used within a certain characteristic text to categorise opinion or perception. The data set includes a number of algorithms for machine learning, and results are compared with the Decision Tree, Naïve-Bayes classifiers that are evaluated according to such criteria as recall, precision and F-score. The article is based on a range of methods of classification in order to decide whether or not an individual is unwanted, constructive or impersonal in terms of his or her opinions, and forecasts a product's star ranking. There are also two specialised approaches such as the classification of features followed by the classification of polarisation along with test findings. Finally, a comparative study is conducted in this paper between 3 classification methods. 1) Decision Tree 2) Novel Bag-Boost algorithm of classification 3) Naive-Bayer's, where high accuracy is compared to the other two. Where the hybrid Novel algorithm gave high accuracy in comparison with the other two algorithms.