Metadata-Version: 2.1
Name: Orange3-Text-zh
Version: 1.3.3
Summary: 橙现智能文本挖掘插件.
Home-page: https://chengxianzn.one/
Author: Bioinformatics Laboratory, FRI UL
Author-email: info@biolab.si
License: UNKNOWN
Keywords: orange3-text-zh,data mining,orange3 add-on
Platform: UNKNOWN
Description-Content-Type: text/markdown
Requires-Dist: Orange3-zh (>=3.25.0)
Requires-Dist: beautifulsoup4
Requires-Dist: docx2txt (>=0.6)
Requires-Dist: gensim (>=0.12.3)
Requires-Dist: jieba
Requires-Dist: lxml
Requires-Dist: nltk (>=3.0.5)
Requires-Dist: numpy
Requires-Dist: odfpy (>=1.3.5)
Requires-Dist: paddlepaddle-tiny (==1.6.1)
Requires-Dist: pdfminer3k (>=1.3.1)
Requires-Dist: python-dateutil (<3.0.0)
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: setuptools-git
Requires-Dist: simhash (>=1.11)
Requires-Dist: tweepy
Requires-Dist: wikipedia

Orange3 Text
============

Orange add-on for text mining. It provides access to publicly available data,
like NY Times, Twitter and PubMed. Further, it provides tools for preprocessing,
constructing vector spaces (like bag-of-words, topic modeling and word2vec) and
visualizations like word cloud end geo map. All features can be combined with
powerful data mining techniques from the Orange data mining framework.

See [documentation](http://orange3-text.readthedocs.org/).

Features
--------
#### Access to data
* Load a corpus of text documents
* Access publicly available data (The Guardian, NY Times, Twitter, Wikipedia, PubMed)

#### Text analysis
* Preprocess corpus
* Generate bag of words
* Embed documents into vector space
* Perform sentiment analysis
* Detect emotions in tweets
* Discover topics in the text
* Compute document statistics
* Visualize frequent words in the word cloud
* Find words that enrich selected documents

