Web Crawling: Strategy and Visualization
├── Introduction
│   └── Overview of Web Crawling
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Target Website Selection
├── Developing a Web Crawler
│   ├── Data Retrieval
│   ├── HTML Content Parsing
│   └── Data Storage
├── Visualization with Word Cloud
│   └── Creating a Word Cloud from Crawled Data
└── Conclusion
    └── Ethical Considerations in Web Crawling

1. Introduction

Overview of Web Crawling

• Web Crawling refers to the automated process of visiting web pages and extracting information, a crucial technique in data mining and content analysis.

2. Setting Up the Environment

Importing Libraries

import requests
from bs4 import BeautifulSoup
from wordcloud import WordCloud
import matplotlib.pyplot as plt

Target Website Selection

• Carefully select a target website for crawling, respecting its robots.txt and legal terms.

3. Developing a Web Crawler

Data Retrieval

• Fetching web pages using HTTP requests.

url = '<http://example.com>'
response = requests.get(url)

HTML Content Parsing

• Extracting relevant information from HTML content using BeautifulSoup.

soup = BeautifulSoup(response.content, 'html.parser')
text = soup.get_text()

Data Storage

• Organizing and storing the extracted data for analysis.