Text analysis with python : a research oriented guide / Mamta Mittal [and three others].

Format:

Book

Author/Creator:

Mittal, Mamta, author.

Language:

English

Subjects (All):

Data mining.

Physical Description:

1 online resource (268 pages)

Edition:

First edition.

Place of Publication:

Singapore : Bentham Science Publishers Pte. Ltd., [2022]

Summary:

Text Analysis with Python: A Research-Oriented Guide is a quick and comprehensive reference on text mining using python code. The main objective of the book is to equip the reader with the knowledge to apply various machine learning and deep learning techniques to text data. The book is organized into eight chapters which present the topic in a structured and progressive way. Key Features · Introduces the reader to Python programming and data processing · Introduces the reader to the preliminaries of natural language processing (NLP) · Covers data analysis and visualization using predefined python libraries and datasets · Teaches how to write text mining programs in Python · Includes text classification and clustering techniques · Informs the reader about different types of neural networks for text analysis · Includes advanced analytical techniques such as fuzzy logic and deep learning techniques · Explains concepts in a simplified and structured way that is ideal for learners · Includes References for further reading Text Analysis with Python: A Research-Oriented Guide is an ideal guide for students in data science and computer science courses, and for researchers and analysts who want to work on artificial intelligence projects that require the application of text mining and NLP techniques.

Contents:

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 &amp

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:

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

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

Gensim Library

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

4.3. TEXT MINING PRE-PROCESSING TECHNIQUES

4.4. FEATURE SELECTION IN TEXT MINING

4.5. TEXT SUMMARIZATION

4.6. TEXT EXTRACTION

4.6.1. Bag of Words

Limitations of Bag of Words

4.6.2. TF-IDF

Word2vec

Document Term Matrix

4.7. TEXT VISUALIZATION

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

CONCLUSIONS

CHAPTER HIGHLIGHTS

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

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

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

7.3. PREPROCESSING

7.4. FEATURE EXTRACTION

7.5. FUZZY CLUSTERING

Fuzzy C-Means Clustering

Steps to Perform the fuzzy C-means Clustering Algorithm

Fuzzy K-Means Clustering

7.6. CLASSIFICATION

7.7. FUZZY ASSOCIATION RULES

7.8. FUZZY VISUALIZATION

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

Step 1. Importing the essential python Keras libraries

8.3. FEED FORWARD NEURAL NETWORK

8.4. CONVOLUTION NEURAL NETWORK (CNN)

8.5. Multi-Layer Perceptron (MLP)

8.6. RECURRENT NEURAL NETWORK (RNN)

Text Classification using RNN.

Text Generation using RNN

8.7. LONG SHORT-TERM MEMORY (LSTM)

Text Generation in LSTM

Text Classification using LSTM

Subject Index

Back Cover.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Includes bibliographical references.

ISBN:

9789815049602

9815049607

OCLC:

1344538382