| Description |
1 online resource (xix, 397 pages) : illustrations |
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text file rdaft http://rdaregistry.info/termList/fileType/1002 |
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PDF |
| Note |
Includes index. |
| Summary |
Utilize web scraping at scale to quickly get unlimited amounts of free data available on the web into a structured format. This book teaches you to use Python scripts to crawl through websites at scale and scrape data from HTML and JavaScript-enabled pages and convert it into structured data formats such as CSV, Excel, JSON, or load it into a SQL database of your choice. This book goes beyond the basics of web scraping and covers advanced topics such as natural language processing (NLP) and text analytics to extract names of people, places, email addresses, contact details, etc., from a page at production scale using distributed big data techniques on an Amazon Web Services (AWS)-based cloud infrastructure. It covers developing a robust data processing and ingestion pipeline on the Common Crawl corpus, containing petabytes of data publicly available and a web crawl data set available on AWS's registry of open data. Getting Structured Data from the Internet also includes a step-by-step tutorial on deploying your own crawlers using a production web scraping framework (such as Scrapy) and dealing with real-world issues (such as breaking Captcha, proxy IP rotation, and more). Code used in the book is provided to help you understand the concepts in practice and write your own web crawler to power your business ideas. You will: Understand web scraping, its applications/uses, and how to avoid web scraping by hitting publicly available rest API endpoints to directly get data Develop a web scraper and crawler from scratch using lxml and BeautifulSoup library, and learn about scraping from JavaScript-enabled pages using Selenium Use AWS-based cloud computing with EC2, S3, Athena, SQS, and SNS to analyze, extract, and store useful insights from crawled pages Use SQL language on PostgreSQL running on Amazon Relational Database Service (RDS) and SQLite using SQLalchemy Review sci-kit learn, Gensim, and spaCy to perform NLP tasks on scraped web pages such as name entity recognition, topic clustering (Kmeans, Agglomerative Clustering), topic modeling (LDA, NMF, LSI), topic classification (naive Bayes, Gradient Boosting Classifier) and text similarity (cosine distance-based nearest neighbors) Handle web archival file formats and explore Common Crawl open data on AWS Illustrate practical applications for web crawl data by building a similar website tool and a technology profiler similar to builtwith.com Write scripts to create a backlinks database on a web scale similar to Ahrefs.com, Moz.com, Majestic.com, etc., for search engine optimization (SEO), competitor research, and determining website domain authority and ranking Use web crawl data to build a news sentiment analysis system or alternative financial analysis covering stock market trading signals Write a production-ready crawler in Python using Scrapy framework and deal with practical workarounds for Captchas, IP rotation, and more. |
| Contents |
Chapter 1: Introduction to Web Scraping -- Chapter 2: Web Scraping in Python Using Beautiful Soup Library -- Chapter 3: Introduction to Cloud Computing and Amazon Web Services (AWS) -- Chapter 4: Natural Language Processing (NLP) and Text Analytics -- Chapter 5: Relational Databases and SQL Language -- Chapter 6: Introduction to Common Crawl Datasets -- Chapter 7: Web Crawl Processing on Big Data Scale -- Chapter 8: Advanced Web Crawlers -- |
| Subject |
Big data.
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Programming languages (Electronic computers)
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Data mining.
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Automatic data collection systems.
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Data Mining |
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Données volumineuses. |
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Exploration de données (Informatique) |
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Collecte automatique des données. |
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Data mining |
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Automatic data collection systems |
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Big data |
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Programming languages (Electronic computers) |
| Other Form: |
Print version: 9781484265758 |
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Print version: 9781484265772 |
| ISBN |
9781484265765 (electronic bk.) |
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1484265769 (electronic bk.) |
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1484265750 |
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9781484265758 |
| Standard No. |
10.1007/978-1-4842-6576-5 doi |
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