Text Analysis with Python: A Research Oriented Guide

Cover
Title
Copyright
End User License Agreement
Contents
	Preface
	CONSENT FOR PUBLICATION
	CONFLICT OF INTEREST
	ACKNOWLEDGEMENT
Introduction
	1.1. INTRODUCTION
	1.2. NATURAL LANGUAGE
		1.2.1. From Linguistics to Natural Language Processing (NLP)
		1.2.2. Natural Language Processing (NLP)
	1.3. TEXT ANALYSIS
		1.3.1. Advantages
		1.3.2. Methods & Techniques
		1.3.3. Sentiment Analysis (SA)
		1.3.4. Topic Modelling
		1.3.5. Intent Identification
		1.3.6. Keyword Extraction
		1.3.7. Entity Recognition
		1.3.8. Text Analysis Functionality
	1.4. TEXT SUMMARIZATION
		1.4.1. Extraction
		1.4.2. Abstractive Summarization
	1.5. TEXT MINING AND WORKFLOW
		1.5.1. Data Recovery
		1.5.2. Data Extraction
		1.5.3. Data Mining
	CONCLUSION
	REFERENCES
Introduction to Python
	2.1. INTRODUCTION
	2.2. WORKING ENVIRONMENTS OF PYTHON
		Google Colab
		Features of Google Collaboratory (COLAB)
	2.3.WORKING WITH ANACONDA
		Steps to Anaconda Installation
	2.4. CREATING THE FIRST PROJECT IN GOOGLE COLAB CREATING THE FIRST PROJECT IN GOOGLE COLAB CREATING THE FIRST PROJECT IN GOOGLE COLAB CREATING THE FIRST PROJECT IN GOOGLE COLAB
	2.5. MATHEMATICAL OPERATIONS
	2.6. PYTHON LIBRARIES AND CONCEPTS
		Libraries
			a). Math and CMath Libraries
			b). SciPy Library
			c). ScikitLearn Library
			d). NumPy Library
	2.7.BASIC CONCEPTS IN PYTHON
		a). Arrays
		b). Data Frames
		c). Loops
		For loop
		While Loop and the Else Branch
		Program:
	CONCLUSION
	REFERENCES
Data Loading and Pre-Processing
	3.1. INTRODUCTION
	3.1. IMPORTING DATASETS
	3.2. DATA RESHAPING
	3.3. PIVOT AND MELT FUNCTIONS
	3.4. STACKING AND UNSTACKING
	3.5. DATA PRE-PROCESSING
		Outliers
		Missing Value Imputation
		Handling of Missing Data
			Mean Calculation
		Deleting of Specific Row
		Dummy Variables
		One Hot Encoding
	3.6. DATA VISUALIZATION
		• Matplotlib
		• ggplot Visualization
		• Geoplot Visualization
		• Regression Plots
	CONCLUSION
	REFERENCES
Text Mining
	INTRODUCTION
		The Steps Followed for Text Mining are:
		Why Should we use Text Mining?
		Benefits of Text Mining
		Text Analysis in Real-Time
		Text Mining Applications
		Issues in Text Mining
	4.1. TEXT MINING WITH PYTHON
		Program:
		Program:
		Program:
		Gensim Library
		Program:
			Output:
		Program
			Output
	4.2. DATA GATHERING
		Reading a Text File
			Steps for Reading a Text File in Python
		Open() Function
			Syntax
		Reading Text File
		Close ()
		Syntax:close()
		Reading a CSV File
		Steps
		Reading Text from a PDF File
		import PyPDF2
			Program
	4.3. TEXT MINING PRE-PROCESSING TECHNIQUES
		Program:
			Output:
		Program:
			Output
		Program:
		Program:
		Program:
		Output
		Program:
			Output:
		Program:
		Program:
	4.4. FEATURE SELECTION IN TEXT MINING
		Program
			Output:
	4.5. TEXT SUMMARIZATION
		Program
		Program:
	4.6. TEXT EXTRACTION
		4.6.1. Bag of Words
		Program:
			Limitations of Bag of Words
		4.6.2. TF-IDF
		Program
			Output
		Program:
			Output:
		Word2vec
		Program:
			Output
		Document Term Matrix
		Program:
			Output
	4.7. TEXT VISUALIZATION
		Program
			Output
		Program
			Output:
		Program:
			Output
		Program
			Output
		Program
			Output
		Program
			Output
	CONCLUSION
	REFERENCES
Text Classification in Python
	5.1. INTRODUCTION
	5.2. TEXT CLASSIFICATION
	5.3. MACHINE LEARNING-BASED TEXT CLASSIFICATION
		Step by Step Explanation
	5.4. APPLICATIONS OF TEXT MINING
		5.4.1. Email Spam Detection
		5.4.2. Social Media Reviews
		5.4.3. Google Translator
		5.4.4. Text labelling Based on Content
	5.5. CLASSIFICATION ALGORITHMS
		5.5.1.. Naïve Bayes (NB) Classifiers
		Case Study: Text Classification With Naïve Bayes
			Movie Review Classification Dataset
	5.5.2. DECISION TREE CLASSIFIERS
		Case Study Text Classification with Decision Tree Algorithms
		5.5.3. Nearest Neighbour Classifier
			How KNN will Work in Text Classifications
			Useful Information with KNN
			Case Study Text Classification with KNN
		5.5.4. Support Vector Machines
			From Texts to Vectors
			Advantages
		Case Study Text Classification with KNN
	CONCLUSIONS
	CHAPTER HIGHLIGHTS
	REFERENCES
Text Clustering in Python
	6.1. INTRODUCTION
	6.2. CLUSTERING PROCESS
		6.2.1. Word Clustering
		6.2.2. Document Clustering
		6.2.3. Term Frequency-Inverse Document Frequency (tf-idf)
	6.3. APPLICATIONS OF TEXT CLUSTERING IN REAL-TIME
		Identifying Fake News
		Spam Filter
		Marketing and Sales
		Classifying Website Traffic
		Identifying Fraudulent or Criminal Activity
		Document Analysis
	6.4. CLUSTERING ALGORITHMS WITH CODE IMPLEMENTATION
		6.4.1. K-means Clustering
			Advantages
			Disadvantages of k-means Clustering
			K means Clustering in Scikit-learn
		6.4.2. Hierarchical Clustering
			How it Works
			Hierarchical Clustering Applications
			Hierarchical Clustering with Scikit-learn
		6.4.3. Fuzzy C-means Clustering
			Stepwise Approach To Performing fuzzy C-means Clustering
			Fuzzy C means Clustering via Scikit-learn
	CONCLUSIONS
	REFERENCES
Fuzzy Logic in Text Mining Using Python
	7.1. INTRODUCTION TO FUZZY LOGIC
		Steps to be Followed in the Fuzzy System
		Fuzzy Membership Functions
		Trapezoidal Membership Function
		Gaussian Membership Function
		Generalised Bell Membership Function
		Sigmoid Membership Function
		Fuzzy Set Operations
		Why do we use Fuzzy Logic?
		Uses of Fuzzy Logic in Text Mining
		Applications of Fuzzy System
		Issues in Fuzzy Logic
	7.2. FUZZY LOGIC WITH PYTHON
		FuzzyWuzzy Library
		Program
		Program
		Program
		Program
		Program
		Program
	7.3. PREPROCESSING
		Program
		Program
		Program
	7.4. FEATURE EXTRACTION
		Program
		Program
		7.5. FUZZY CLUSTERING
		Fuzzy C-Means Clustering
		Steps to Perform the fuzzy C-means Clustering Algorithm
		Program
		Program
		Fuzzy K-Means Clustering
		Program
	7.6. CLASSIFICATION
		Program
		Program
	7.7. FUZZY ASSOCIATION RULES
		Program
		Program
	7.8. FUZZY VISUALIZATION
		Program
		Program
		Program
		Program:
	CONCLUSION
	CHAPTER HIGHLIGHTS
	REFERENCES
Deep Learning for Text Mining
	8.1. DEEP LEARNING BASICS
		Neuron
		Activation Functions
		Why Deep Learning?
		Limitations of Deep Learning
		Applications of Deep Learning
	8.2. DEEP LEARNING WITH PYTHON
		Keras Library
		Import Keras
			Program
			Step 1. Importing the essential python Keras libraries
			Program
	8.3. FEED FORWARD NEURAL NETWORK
	8.4. CONVOLUTION NEURAL NETWORK (CNN)
		Program
		Program
		8.5. Multi-Layer Perceptron (MLP)
		Program
	8.6. RECURRENT NEURAL NETWORK (RNN)
		Text Classification using RNN.
		Program
		Text Generation using RNN
		Program
	8.7. LONG SHORT-TERM MEMORY (LSTM)
		Text Generation in LSTM
		Text Classification using LSTM
	CONCLUSION
	CHAPTER HIGHLIGHTS
	REFERENCES
	Subject Index
	Back Cover