Natural Language Processing
Abstract
This book chapter focuses on natural language processing (NLP) and covers various applications such as classifying messages as spam or ham, sentiment analysis, and the use of Bidirectional Encoder Representations from Transformers (BERT). The chapter begins with the task of classifying messages as spam or ham, providing insights into the techniques and approaches used in NLP for this purpose. Sentiment analysis is then discussed, addressing the analysis of emotions and opinions expressed in text data. It explores methods to classify sentiment polarity and sentiment strength. The chapter introduces BERT, a state-of-the-art transformer-based model for NLP tasks. BERT's functionality is explained, including its installation and training for binary text classification using TensorFlow. The utilization of BERT for text summarization and question answering is also explored. Overall, this chapter provides a comprehensive overview of NLP techniques and their applications, including spam classification, sentiment analysis, and the usage of BERT for various NLP tasks.