It's worth noting for future reference that in terms of supervised learning of labels given a text document input, fasttext (https://github.com/facebookresearch/fastText) is leagues ahead of conventional approaches in both accuracy and training speed, and there is a Python interface (https://github.com/salestock/fastText.py) for use with Django/Flask (unfortunately, recent fasttext changes have broken the interface for now).
Re:How we built Tagger News: machine learning on a tight sch...
by snissn on Monday May 22, 2017 @03:16PM
(#14396165)
Can you suggest any unsupervised learning? I want to take a body of text associated with users and come up with keywords/topics with each user. Thanks! :)
Re:How we built Tagger News: machine learning on a tight sch...
by capybara on Monday May 22, 2017 @03:50PM
(#14396401)
Some fairly widely-used techniques include LSI, LDA, and word2vec or doc2vec. There a lot of different techniques out there! I'm one of the creators of Tagger News, and we used LDA with python's Gensim package. Here's a good tutorial: https://radimrehurek.com/gensim/tut2.html
It's worth noting for future reference that in terms of supervised learning of labels given a text document input, fasttext (https://github.com/facebookresearch/fastText) is leagues ahead of conventional approaches in both accuracy and training speed, and there is a Python interface (https://github.com/salestock/fastText.py) for use with Django/Flask (unfortunately, recent fasttext changes have broken the interface for now).
[1]: https://twitter.com/yoavgo/status/751178795323908096
[2]: https://nlpers.blogspot.com/2016/08/fast-easy-baseline-text-...