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https://github.com/biolab/orange3-text
🍊 :page_facing_up: Text Mining add-on for Orange3
https://github.com/biolab/orange3-text
bag-of-words lemmatization newspapers nltk orange sentiment-analysis stemming stopwords text text-analysis text-mining twitter
Last synced: about 2 months ago
JSON representation
🍊 :page_facing_up: Text Mining add-on for Orange3
- Host: GitHub
- URL: https://github.com/biolab/orange3-text
- Owner: biolab
- License: other
- Created: 2015-06-26T12:02:06.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2024-10-11T10:04:43.000Z (3 months ago)
- Last Synced: 2024-10-29T20:54:53.113Z (2 months ago)
- Topics: bag-of-words, lemmatization, newspapers, nltk, orange, sentiment-analysis, stemming, stopwords, text, text-analysis, text-mining, twitter
- Language: Python
- Homepage:
- Size: 46.4 MB
- Stars: 127
- Watchers: 20
- Forks: 84
- Open Issues: 56
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
- project-awesome - biolab/orange3-text - 🍊 :page_facing_up: Text Mining add-on for Orange3 (Python)
README
Orange3 Text
============
[](https://discord.gg/FWrfeXV)
[](https://travis-ci.org/biolab/orange3-text)
[](https://codecov.io/gh/biolab/orange3-text)
[](http://orange3-text.readthedocs.org/en/latest/?badge=latest)
Orange3 Text extends [Orange3](http://orange.biolab.si), a data mining software
package, with common functionality for text mining. It provides access
to publicly available data, like NY Times, Twitter, Wikipedia and PubMed. Furthermore,
it provides tools for preprocessing, constructing vector spaces (like
bag-of-words, topic modeling, and similarity hashing) and visualizations like word cloud
end geo map. All features can be combined with powerful data mining techniques
from the Orange data mining framework.
Please note that Text add-on won't work on 32-bit Windows systems. The add-on depends on conda-forge and they have [removed support for Windows 32](https://github.com/conda-forge/staged-recipes/issues/5640) in April 2018.
Anaconda installation
---------------------
The easiest way to install Orange3-Text is with Anaconda distribution. Download [Anaconda](https://www.continuum.io/downloads)
for your OS (Python version 3.5). In your Anaconda Prompt first add conda-forge to your channels:
conda config --add channels conda-forge
Then install Orange3-Text
conda install orange3-text
Run
python -m Orange.canvas
to open Orange and check if everything is installed properly.
Installation from source
------------------------
To install the add-on from source
# Clone the repository and move into it
git clone https://github.com/biolab/orange3-text.git
cd orange3-text
# Install the dependencies:
pip install -r requirements.txt
# Finally install Orange3-Text in editable/development mode.
pip install -e .
To register this add-on with Orange, but keep the code in the development directory (do not copy it to
Python's site-packages directory), run
python setup.py develop
Windows setup for biopython library
-----------------------------------
If you're not using Anaconda distribution, you can manually install biopython library before installing the add-on.
First, download the compiler [Visual Studio](http://landinghub.visualstudio.com/visual-cpp-build-tools) and run the setup with:
python setup.py build_ext --inplace --compiler=msvc install
Usage
-----
After the installation, the widgets from this add-on are registered with Orange. To run Orange from the terminal,
use
python3 -m Orange.canvas
or
orange-canvas
The new widgets are in the toolbox bar under Text Mining section.