  Today's business ecosystem has become very competitive. Customer satisfaction
has become a major focus for business growth. Business organizations are
spending a lot of money and human resources on various strategies to understand
and fulfill their customer's needs. But, because of defective manual analysis
on multifarious needs of customers, many organizations are failing to achieve
customer satisfaction. As a result, they are losing customer's loyalty and
spending extra money on marketing. We can solve the problems by implementing
Sentiment Analysis. It is a combined technique of Natural Language Processing
(NLP) and Machine Learning (ML). Sentiment Analysis is broadly used to extract
insights from wider public opinion behind certain topics, products, and
services. We can do it from any online available data. In this paper, we have
introduced two NLP techniques (Bag-of-Words and TF-IDF) and various ML
classification algorithms (Support Vector Machine, Logistic Regression,
Multinomial Naive Bayes, Random Forest) to find an effective approach for
Sentiment Analysis on a large, imbalanced, and multi-classed dataset. Our best
approaches provide 77% accuracy using Support Vector Machine and Logistic
Regression with Bag-of-Words technique.
