10. Machine Learning in Web Scraping

Lecture Notes: Introduction to Machine Learning in Web Scraping

1. Introduction to Machine Learning in Web Scraping

Machine learning (ML) enhances web scraping by automating complex tasks, improving data accuracy, and enabling advanced analysis. This lecture introduces ML concepts applied to web scraping, focusing on Natural Language Processing (NLP), data classification, and AI-driven scraping tools.

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2. Using NLP for Data Extraction (e.g., Named Entity Recognition)

Objective

Extract meaningful entities such as names, dates, locations, and organizations from unstructured web data using NLP techniques like Named Entity Recognition (NER).

Challenges

1. Unstructured and noisy text data.
2. Contextual understanding of extracted entities.

Solution

Use Python libraries such as spaCy or nltk to implement NER.

Code Example

import spacy

# Load spaCy's English NLP model
nlp = spacy.load("en_core_web_sm")

# Sample text
data = "OpenAI was founded in San Francisco in 2015. It specializes in AI research."

# Process the text
doc = nlp(data)

# Extract named entities
print("Entities detected:")
for ent in doc.ents:
    print(f"{ent.text} ({ent.label_})")

Output

Entities detected:
OpenAI (ORG)
San Francisco (GPE)
2015 (DATE)

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3. Automating Data Classification and Categorization

Objective

Classify scraped data into predefined categories or labels using supervised ML models.

Challenges

1. Labeling training data for supervised learning.
2. Balancing accuracy and computational efficiency.

Solution

Train a model using scikit-learn to classify scraped data into categories.

Code Example

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import train_test_split

# Sample data
data = [
    "Buy the latest iPhone at a discounted price",
    "New Samsung Galaxy released this month",
    "Breaking news: AI beats humans at chess",
    "Sports update: Local team wins championship"
]
labels = ["ecommerce", "ecommerce", "news", "sports"]

# Split data
X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.25, random_state=42)

# Build a classification pipeline
model = make_pipeline(TfidfVectorizer(), MultinomialNB())
model.fit(X_train, y_train)

# Test the model
predictions = model.predict(X_test)
print("Predictions:", predictions)

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4. Introduction to AI-Driven Scraping Tools

Objective

Leverage AI-powered scraping tools that can dynamically adapt to website changes and bypass anti-scraping mechanisms.

Examples of AI-Driven Tools

1. Diffbot: Extracts structured data from web pages using AI.
2. Scrapy with AI Plugins: Combines traditional scraping with ML capabilities.
3. Apify AI Tools: Provides intelligent automation for complex scraping tasks.

Code Example: Using Diffbot API

import requests

# Diffbot API endpoint and token
API_TOKEN = "your_diffbot_api_token"
url = "https://example.com/article"
api_endpoint = f"https://api.diffbot.com/v3/article?token={API_TOKEN}&url={url}"

# Send request
response = requests.get(api_endpoint)

# Parse response
if response.status_code == 200:
    article_data = response.json()
    print("Title:", article_data['objects'][0]['title'])
    print("Author:", article_data['objects'][0]['author'])
    print("Text:", article_data['objects'][0]['text'])
else:
    print("Error:", response.status_code)

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5. Conclusion

Machine learning significantly enhances web scraping capabilities. Techniques like NLP, classification, and AI-driven tools allow for more intelligent and automated data extraction, making them invaluable for large-scale and complex projects.

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Assignments and Quiz

Assignment: Implement an NER model to extract names, organizations, and dates from a sample news article. Use spaCy or a similar NLP library. Save the results in JSON format.

Quiz Questions:

1. What is Named Entity Recognition (NER) used for in web scraping?
2. Name one library used for implementing NER in Python.
3. What are the main challenges in automating data classification?
4. What is the advantage of AI-driven scraping tools?
5. Provide an example of an AI-driven scraping tool.