A webmining CLI tool & library for python.
Project description
minet is a webmining command line tool & library for python (>= 3.6) that can be used to collect and extract data from a large variety of web sources such as raw webpages, Facebook, CrowdTangle, YouTube, Twitter, Media Cloud etc.
It adopts a very simple approach to various webmining problems by letting you perform a variety of actions from the comfort of your command line. No database needed: raw data files such as CSV should be sufficient to do the work.
In addition, minet also exposes its high-level programmatic interface as a python library so you can tweak its behavior at will.
Shortcuts: Command line documentation, Python library documentation.
Summary
- What it does
- Documented use cases
- Features (from a technical standpoint)
- Installation
- Upgrading
- Uninstallation
- Documentation
- Contributing
- How to cite
What it does
Minet can single-handedly:
- Extract URLs from a text file (or a table)
- Parse URLs (get useful information, with Facebook- and Youtube-specific stuff)
- Join two CSV files by matching the columns containing URLs
- From a list of URLs, resolve their redirections
- ...and check their HTTP status
- ...and download the HTML
- ...and extract hyperlinks
- ...and extract the text content and other metadata (title...)
- ...and scrape structured data (using a declarative language to define your heuristics)
- Crawl (using a declarative language to define a browsing behavior, and what to harvest)
- Mine or search:
- Crowdtangle (requires API access)
- Mediacloud (requires free API access)
- Twitter (requires free API access)
- Youtube (requires free API access)
- Scrape (without requiring special access):
- Google Drive (spreadsheets etc.)
- Grab & dump cookies from your browser
- Dump Hyphe data
Documented use cases
- Fetching a large amount of urls
- Joining 2 CSV files by urls
- Using minet from a Jupyter notebook (very useful to experiment with the tool or teach students)
- Scraping DSL Tutorial
Features (from a technical standpoint)
- Multithreaded, memory-efficient fetching from the web.
- Multithreaded, scalable crawling using a comfy DSL.
- Multiprocessed raw text content extraction from HTML pages.
- Multiprocessed scraping from HTML pages using a comfy DSL.
- URL-related heuristics utilities such as extraction, normalization and matching.
- Data collection from various APIs such as CrowdTangle.
Installation
minet can be installed as a standalone CLI tool (currently only on mac, ubuntu & similar) by running the following command in your terminal:
curl -sSL https://raw.githubusercontent.com/medialab/minet/master/scripts/install.sh | bash
Don't trust us enough to pipe the result of a HTTP request into bash? We wouldn't either, so feel free to read the installation script here and run it on your end if you prefer.
On ubuntu & similar you might need to install curl and unzip before running the installation script if you don't already have it:
sudo apt-get install curl unzip
Else, minet can be installed directly as a python CLI tool and library using pip:
pip install minet
If you need more help to install and use minet from scratch, you can check those installation documents.
Finally if you want to install the standalone binaries by yourself (even for windows) you can find them in each release here.
Upgrading
To upgrade the standalone version, simply run the install script once again:
curl -sSL https://raw.githubusercontent.com/medialab/minet/master/scripts/install.sh | bash
To upgrade the python version you can use pip thusly:
pip install -U minet
Uninstallation
To uninstall the standalone version:
curl -sSL https://raw.githubusercontent.com/medialab/minet/master/scripts/uninstall.sh | bash
To uninstall the python version:
pip uninstall minet
Documentation
Contributing
To contribute to minet you can check out this documentation.
How to cite
minet is published on Zenodo as
You can cite it thusly:
Guillaume Plique, Pauline Breteau, Jules Farjas, Héloïse Théro, & Jean Descamps. (2019, October 14). Minet, a webmining CLI tool & library for python. Zenodo. http://doi.org/10.5281/zenodo.4564399
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