Text Analysis with Python: A Research Oriented Guide

In this chapter, the basic idea of what text data is, along with the definition of natural language and techniques involved in it has been clearly explained. Besides this, the framework of text mining and its workflow framework is explained.

This chapter discusses a brief introduction to the python programming language, the core Python language, and important libraries for natural language processing by using different IDEs like Anaconda and Google Co laboratory.

This chapter provides details about importing some predefined Python libraries, various data pre-processing techniques, and visualization techniques using various modules in python.

This chapter presents text mining and its importance, different open-source libraries for natural language processing, data collection, preprocessing concepts, feature selection, and feature extraction techniques, along with visualization techniques.

Text classification is an ML technique that assigns a set of predefined tags to open text, and text classifiers have been used to structure, organize, and categorize any sort of text data ranging from documents, files, medical studies, to the overall web. This chapter will provide you with the text classification techniques in python. In addition, it focuses on training a supervised learning model to do text classification with a real-time case study.

In this chapter, we learn about clustering and how document and text clustering can be performed. This chapter explains the real-time applications of text clustering and the differences between soft and hard clustering types. The clustering algorithms, including KNN, hierarchical and Fuzzy clustering, were used . Fuzzy clustering or soft clustering types can add better value performance-wise than the other two clustering algorithms. Besides, we also presented how to conduct text clustering in python using unsupervised machine learning techniques. To explain this in detail, the IRIS dataset is considered famous in UCI Machine learning Repository and well presented with python script.

This chapter introduces about fuzzy logic, its membership functions, and rules followed by various text mining concepts using the fuzzy logic using python. Different open-source libraries for fuzzy logic, preprocessing concepts in fuzzy logic text mining, different feature extraction techniques, fuzzy clustering concepts, classification in fuzzy text mining, fuzzy association rule mining concepts, and visualization in fuzzy text mining are presented.

This Chapter presents about deep learning basics, deep learning implementation in Python, and text mining concepts utilising several deep learning models such as Feed Forward Neural Network, Convolutional Neural Network, Multi- Layer Perceptron, Recurrent Neural Network, and Long Short-Term Memory.