Practical Text Analysis using Deep Learning

Practical Text Analysis using Deep Learning by Michael Fire.

From the post:

Deep Learning has become a household buzzword these days, and I have not stopped hearing about it. In the beginning, I thought it was another rebranding of Neural Network algorithms or a fad that will fade away in a year. But then I read Piotr Teterwak’s blog post on how Deep Learning can be easily utilized for various image analysis tasks. A powerful algorithm that is easy to use? Sounds intriguing. So I decided to give it a closer look. Maybe it will be a new hammer in my toolbox that can later assist me to tackle new sets of interesting problems.

After getting up to speed on Deep Learning (see my recommended reading list at the end of this post), I decided to try Deep Learning on NLP problems. Several years ago, Professor Moshe Koppel gave a talk about how he and his colleagues succeeded in determining an author’s gender by analyzing his or her written texts. They also released a dataset containing 681,288 blog posts. I found it remarkable that one can infer various attributes about an author by analyzing the text, and I’ve been wanting to try it myself. Deep Learning sounded very versatile. So I decided to use it to infer a blogger’s personal attributes, such as age and gender, based on the blog posts.

If you haven’t gotten into deep learning, here’s another opportunity focused on natural language processing. You can follow Michael’s general directions to learn on your own or follow more detailed instructions in his Ipython notebook.


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