Text Analysis with Python: A Research-Oriented Guide

Table of Contents

1. Contents Preface
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

Chapter 2 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

Chapter 3 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

Chapter 4 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

Chapter 5 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

Chapter 6 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

Author

• Mamta Mittal
• Gopi Battineni
• Bhimavarapu Usharani
• Lalit Mohan Goyal