Amazon Product review Sentiment Analysis using BERT Natural Language processing, a sub-field of machine learning has gained immense popularity in the last 5 years in both research and industrial applications due to the advancement in the field of deep learning and improvement in the computational power of hardware systems. It is a technique for computers to understand how human languages work involving the usage of computational linguistics and the computer science domain. In recent years, NLP has found its usage in several applications related to understanding and interpreting text, audio, and video files. df["Sentiment"] = df["data/star_rating"].apply(lambda score: "positive" if score >= 3 else "negative") In our dataset, we still have a large corpus of reviews provided by the customer. Since BERT requires high computational power and takes a large amount of time to train on the data frame, we will drop some rows from our dataset to reduce the time for training on it. The tokenizer will convert each word in a sentence into integer tokens/IDs based on the frequency of the word appearing in the corpus. training_labels padding=True, label = tf.argmax(tf_prediction, axis=1) designer bags cyber monday