Web Data Mining with Python: Discover and extract information from the web using Python (English Edition)

✍ Scribed by Dr. Ranjana Rajnish, Dr. Meenakshi Srivastava

• Publisher: BPB Publications
• Year: 2023
• Tongue: English
• Leaves: 308
• Category: Library

✦ Synopsis

Explore different web mining techniques to discover patterns, structures, and information from the web

Key Features

● A complete overview of the basic and advanced concepts of Web mining.

● Work with easy-to-use open-source Python libraries for Web mining.

● Get familiar with the various beneficial areas and applications of Web mining.

Description

Data Science is the fastest growing job across the globe and is predicted to create 11.5 million jobs by 2026, so job seekers with this skill set have a lot of opportunities. One of the most sought areas in the field of Data Science is mining information from the web. If you are an aspiring Data Scientist looking to learn different Web mining techniques, then this book is for you.

This book starts by covering the key concepts of Web mining and its taxonomy. It then explores the basics of Web scraping, its uses and components followed by topics like legal aspects related to scraping, data extraction and pre-processing, scraping dynamic websites, and CAPTCHA. The book also introduces you to the concept of Opinion mining and Web structure mining. Furthermore, it covers Web graph mining, Web information extraction, Web search and hyperlinks, Hyperlink Induced Topic Search (HITS) search, and partitioning algorithms that are used for Web mining. Towards the end, the book will teach you different mining techniques to discover interesting usage patterns from Web data.

By the end of the book, you will master the art of data extraction using Python.

What you will learn

● Learn how to scrape data from any website with Python.

● Get familiar with the concepts of Opinion Mining and Sentiment Analysis.

● Use Web structure mining to discover structure information from the web.

● Learn how to collect and analyze social media data using Python.

● Use Web usage mining for predicting users' browsing behaviors.

Who this book is for

The book is for anyone who wants to learn Web mining. Aspiring Data Scientists, Data Engineers, and Data Analysts who want to master Web mining will find this book very helpful.

Table of Contents

1. Web Mining—An Introduction

2. Web Mining Taxonomy

3. Prominent Applications with Web Mining

4. Python Fundamentals

5. Web Scraping

6. Web Opinion Mining

7. Web Structure Mining

8. Social Network Analysis in Python

9. Web Usage Mining

✦ Table of Contents

book Title
Inner title
Copyright
Dedicated
About the Authors
About the Reviewer
Acknowledgements
Preface
Coloured Images
Piracy
Table of Contents
Chapter 1: Web Mining—An Introduction
Introduction
Structure
Objectives
Introduction to Web mining
World Wide Web
Evolution of the World Wide Web
Internet and Web 2.0
An overview of data mining, modeling, and analysis
Basics of Web mining
Categories of Web mining
Difference between data mining and Web mining
Applications of Web mining
Web mining and Python
Essential Python libraries for Web mining
How Python is helpful in Web mining?
Conclusion
Points to Remember
Multiple Choice Questions
Answer
Questions
Key terms
Chapter 2: Web Mining Taxonomy
Introduction
Structure
Objective
Introduction to Web mining
Web content mining
Basic application areas of Web content mining
Contents of a web page
Content pre-processing
Web content analysis
Web structure mining
Web usage mining
Key concepts
Ranking metrics
Page rank
Hubs and Authorities
Web Robots
Information Scent
User Profile
Online bibliometrics
Types of Bibliometric measures
Conclusion
Points to remember
Multiple Choice Questions
Answers
Questions
Key terms
Chapter 3: Prominent Applications with Web Mining
Introduction
Structure
Objectives
Personalized customer applications—E-commerce
Web search
Most common methods of website tracking
Personalized portal and Web
Web service performance optimization
Bounce rate
Average time on page
Unique visitors
Process mining
Concepts of association rules
Association rule mining
Components of Apriori algorithm
Support and frequent itemsets
Confidence
Lift
Steps in apriori algorithm
Concepts of sequential pattern
Sequence database
Subsequence versus supersequence
Minimum support
Prefix and suffix
Projection
Association rule mining and python libraries
Pandas
Mlxtend
Conclusion
Points to remember
Multiple Choice Questions
Answer
Questions
Key terms
Chapter 4: Python Fundamentals
Introduction
Structure
Objectives
Introduction to Python
Basics of Python
Python programming
Writing “Hello World”, the first Python script
Conditional/selection statements
Looping/iterative constructs
Functions
Lists
Basics of HTML: inspecting a Web page
Basics of Python libraries
Installation of Python
Unix and Linux platform
Windows Platform
Introduction to commonly used IDE’s and PDE
Integrated development learning environment (IDLE)
Atom
Sublime text
PyDev
Spyder (the scientific Python development environment)
PyCharm
Google Colab
Installation of Anaconda
Conclusion
Points to remember
Multiple choice questions
Answers
Chapter 5: Web Scraping
Introduction
Structure
Objectives
Introduction to Web scraping
Web scraping
Uses of Web scraping
Working of Web scraper
Challenges Of Web Scraping
Python modules used for scraping
Legality of Web scraping
Data extraction and preprocessing
Handling text, image, and videos
Handling text
Handling images
Extracting videos from a Web page
Scraping dynamic websites
Dealing with CAPTCHA
Case study: Implementing Web scraping to develop a scraper for finding the latest news
Conclusion
Points to remember
Multiple choice questions
Answers
Questions
Key terms
Chapter 6: Web Opinion Mining
Introduction
Structure
Objectives
Concepts of opinion mining
NLTK for sentiment analysis
Opinion Mining/Sentiment Analysis at different levels
Collection of reviewFor the task of Sentiment Analysis, the co
Data sources for opinion mining
Working with data
Pre-processing of data
Tokenization
Part of Speech tagging
Feature extraction
Bag-of-Words
TF-IDF
Case study for Sentiment Analysis
Conclusion
Points to remember
Multiple choice questions
Answers
Questions
Key terms
Chapter 7: Web Structure M
ining
Introduction
Structure
Objectives
Introduction to Web structure mining
Concepts of Web structure mining
Web structure mining
Web graph mining
Web information extraction
Deep Web mining
Web Search and Hyperlinks
Hyperlink analysis on the Web
Hyperlink Induced Topic Search (HITS)
Partitioning algorithm
Implementation in Python
Conclusion
Points to remember
MCQs
Answers
Questions
Key terms
Chapter 8: Social Network Analysis in Python
Introduction
Structure
Objectives
Introduction to Social Network Analysis
Creating a network
Types of graphs
Analyzing network
Distance measures in network connectivity
Distance
Average distance
Eccentricity
Diameter
Radius
Periphery
Center
Network influencers
Case study on Facebook dataset
Conclusion
Points to remember
Multiple choice questions
Answers
Questions
Key terms
Chapter 9: Web Usage Mining
Introduction
Structure
Objectives
Process of Web usage mining
Sources of data
Types of data
Usage data
Content data
Structure data
User data
Key elements of Web usage data pre-processing
Data cleaning
User identification
Session identification
Path identification
Data modeling
Association rule mining
Sequential pattern
Clustering
Classification mining
Discovery and analysis of pattern
Association rule for knowledge discovery
Pattern discovery through clustering
Sequential pattern mining for knowledge discovery
Learning through classification
Pattern analysis
Predictions on transaction pattern
Building a content-based recommendation system
Item profile
User profile
Conclusion
Points to remember
Multiple choice questions
Answers
Questions
Key terms
Index
Back title