In the previous chapter, we learned about advanced vector representation methodologies such as Doc2Vec and Sent2Vec, which significantly improve text processing accuracy. In this chapter, we will explore the applications of Machine Learning (ML) algorithms in Natural Language Processing (NLP). We will start with a gentle introduction to ML and learn about some additional preprocessing steps required for ML model training. We will then gain a thorough understanding of Naive Bayes and Support Vector Machine (SVM) algorithms and build a sentiment analyzer using them. By the end of this chapter, you will have gained a sound understanding of the application of ML algorithms for text processing and will be able to build a production-ready ML-based sentiment analyzer.
The following topics will be covered in this chapter:
- Introduction...
