Text Classification with NLP: Tf-Idf vs Word2Vec vs BERT

In this article, using NLP and Python, I will explain 3 different strategies for text multiclass classification: the old-fashioned Bag-of-Words (with Tf-Idf), the famous Word Embedding (with Word2Vec), and the cutting edge Language models (with BERT). NLP (Natural Language Processing) is the field of artificial intelligence that studies the interactions between computers and human languages, in particular how to program computers to process and analyze large amounts of natural language data. NLP is often applied for classifying text data. Text classification is the problem of assigning categories to text data according to its content. There are different techniques to extract information from raw text data and use it to train a classification model.