Text Analysis with Python: A Research Oriented Guide
- Length: 266 pages
- Edition: 1
- Language: English
- Publisher: Bentham Science Publishers
- Publication Date: 2022-08-12
- ISBN-10: 9815049623
- ISBN-13: 9789815049626
- Sales Rank: #0 (See Top 100 Books)
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.
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
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