This book is meant for those who are interested in learning about natural language processing methods such text classification, parts of speech recognition, topic modelling, text summarization, and sentiment analysis. With the help of this book’s many coding exercises and problem-solution methodology, you can quickly implement Python natural language processing applications. You will have a toolkit of solutions to use on your own projects in the real world after utilizing this book, cutting down on the time it takes to design new initiatives. Additionally, you’ll discover how machine learning and deep learning are used in numerous natural language processing applications. You’ll encounter real-world examples of both semantic and syntactic text analysis, as well as sophisticated approaches to natural language processing like text normalization, sophisticated preprocessing, POS tagging, parsing, text summarization, and sentiment analysis.
Natural language processing:
The field of computer science known as “natural language processing” (NLP) is more particularly the field of “artificial intelligence” (AI) that is concerned with providing computers the capacity to comprehend written and spoken words in a manner similar to that of humans. Computer programs that translate text between languages, reply to spoken commands, and quickly summarize vast amounts of text even in real time are all powered by NLP. You’ve probably used NLP in the form of voice activated GPS devices, digital assistants, speech-to-text dictation programs, customer service chatbots, and other consumer conveniences. The use of NLP in corporate solutions, however, is expanding as a means of streamlining business operations, boosting worker productivity, and streamlining mission-critical business procedures. Natural language processing can be used to evaluate huge amounts of text data, including social media comments, customer service issues, online reviews, news articles, and more, which is one of the key reasons it is so important for organizations. All of this business data has a wealth of insightful information, and NLP can help organizations quickly identify those insights. It accomplishes this by enabling robots to understand human language more quickly, precisely, and consistently than human agents.
Topics covered by this book:
- The different types of text data sources and methods of extraction that can provide information or insights for businesses will be covered in chapter 1.
- The preprocessing of text data using a variety of methodologies and approaches, as well as exploratory data analysis, will be covered in chapter 2.
- We’ll explore a range of feature engineering (text to features) techniques in chapter 3. You will feel confident using the recipes after this chapter.
- Chapter 4 will cover a variety of advanced NLP techniques, some of the advanced NLP applications with the solution strategy and implementation, and how to use machine learning algorithms to extract information from text input.
- We will put into practice end-to-end solutions for a few of the NLP-related industry applications in chapter 5.
- We will use deep learning for NLP in chapter 6.