# PLEASE UPVOTE if you find it useful !!!! Analyzing sentiments related to various products such as Tablet, Mobile and various other gizmos can be fun and difficult especially when collected across various demographics around the world. In this dataset develop a machine learning model to accurately classify various products into 4 different classes of sentiments based on the raw text review provided by the user. Analyzing these sentiments will not only help serve the customers better but can also reveal lolot of customer traits present/hidden in the reviews. The sentiment analysis requires a lot to be taken into account mainly due to the preprocessing involved to represent raw text and make them machine-understandable. Usually, we stem and lemmatize the raw information and then represent it using TF-IDF, Word Embeddings, etc. However, provided the state-of-the-art NLP models such as Transformer based BERT models one can skip the manual feature engineering like TF-IDF and Count Vectorizers.