README.md

aws-blog-parser

This project was developed as a means to extract the contents of each Amazon Web Services (AWS) Blog for the purposes of reusing the contents of the blog posts as sample data within other workloads (i.e. NLP, topic extraction, etc.).

Getting Started

Use the following instructions for installing and running this script.

Prerequisites

Before downloading this repo and running the script, you will need to install the following libraries:

$ pip install requests BeautifulSoup4

Installation

To use this script, clone a copy of this repo:

$ git clone https://github.com/triggan/aws-blog-parser.git
$ cd aws-blog-parser

Running the Script

To run the script, use the following syntax:

usage: aws-blog-parser.py [-h] [-f] blogurl

Extract contents of an AWS blog. As of July 12th, 2018.

positional arguments:
  blogurl     URL of AWS blog. Ex: http://aws.amazon.com/blogs/database/

optional arguments:
  -h, --help  show this help message and exit
  -f          Output each blog post to a separate .json file.

Example:

$ python aws-blog-parser.py -f https://aws.amazon.com/blogs/architecture/

Script Output

If the -f flag is not provided, the script will provide output in the following format via STDOUT.

{
    posts: [
        {
            url: <blog post url>,
            title: <blog post title>,
            authors: [
                <blog author 1>,
                <blog author 2>,
                ...
                <blog author n>
            ],
            date: <blog published date>,
            tags: [
                <blog category tag 1>,
                <blog category tag 2>,
                ...
                <blog category tag n>
            ],
            post: <blog post text content>
        }
    ]
}

If the -f flag is provided, the parsed output of each blog article will be written to separate .json files per blog post.

Authors

• Taylor Riggan - @triggan

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgements

• This blog post helped tremendously while I was developing this script: https://realpython.com/python-web-scraping-practical-introduction/