Comparative Sentiment Analysis using Difference Types of Machine Learning Algorithm

In today's world business are becoming online based. Companies sell their products and seek for consumer's feedback. When all the consumer writes their review about that's a product, It's becomes difficult to say that product is good or not based on their review. That's where Deep learning come. By using this, we can extract opinion or sentiment from the text which is written by the consumer. This is sentiment analysis. It can classify the emotional status of that review. Our project detects opinion from consumer's review whether it is good or bad. We use SVM, Naive Bayes algorithm and some methods. We use the Naive Bayes algorithm because we want to know how often words occur in the document. And then we use SVM for classifying whether words are positive or negative. For our researching purpose, we use the Amazon consumer review data set, which was available online. Some methods that we are using for preprocessing and cleaned the document where just words are left. We trained our model so well with twenty-four thousand data. So, it will give us the best accuracy and we make this model with the best algorithm and after that, it gives the accuracy of 98.39%. This project will help us in real life when we are having trouble with product reviews. Our machine will help us to determine which review is good and which review is bad and make a category of a positive and negative review and saves our time.