Feature Engineering Bookcamp

5 Natural language processing: Classifying social media sentiment

This chapter covers

• Preparing text vectorization for quantitative features
• Practicing cleaning and tokenizing raw text into features
• Extracting and learning features with deep learning
• Taking advantage of transfer learning with BERT

Our last two case studies focused on completely different domains but had a major component in common: we were working with structured tabular data. In the next two case studies, we are going to look at special cases where we need to deploy specific feature engineering techniques to make machine learning possible. In this case study, we will be looking at techniques from the world of natural language processing (NLP), which is a branch of ML focused ...