Demo Day Page Google Classifier
| Demo Day Page Google Classifier | |
|---|---|
| Project Information | |
| Project Title | Demo Day Page Google Classifier |
| Owner | Kyran Adams |
| Start Date | 2/5/2018 |
| Deadline | |
| Keywords | Accelerator, Demo Day, Google Result, Word2vec, Tensorflow |
| Primary Billing | |
| Notes | |
| Has project status | Active |
| Is dependent on | Accelerator Seed List (Data), Demo Day Page Parser |
| Copyright © 2016 edegan.com. All Rights Reserved. | |
Project
This is a tensorflow project that classifies webpages as a demo day page containing a list of cohort companies, currently using scikit learn's random forest model and a bag of words approach. The classifier itself takes:
Features: The number of times each word in words.txt occurs in the titles or headers of a webpage. This is calculated by web_demo_features.py in the same directory. It also takes the number of occurrences of years from 1900-2099, month words grouped in seasons, and phrases of the form "# startups". It also takes the number of simple links (links in the form www.abc.com or www.abc.org) and the number of those that are attached to images. It also takes the number of "strong" html tags in the body.
A frequency matrix of up to 100000 of the most frequent words in the body is also generated.
Training classifications: A set of webpages hand-classified as to whether they contain a list of cohort companies. This is stored in classification.txt, which is a tsv equivalent of Demo Day URLS.xlsx. Keep in mind that this txt file must be utf-8 encoded. In textpad, one can convert a file to utf-8 by pressing save-as, and changing the encoding at the bottom.
A demo day page is an advertisement page for a "demo day," which is a day that cohorts graduating from accelerators can pitch their ideas to investors. These demo days give us a good idea of when these cohorts graduated from their accelerator.
Project location:
E:\McNair\Projects\Accelerators\Spring 2018\demo_day_classifier\
Training data:
E:\McNair\Projects\Accelerators\Spring 2018\demo_day_classifier\DemoDayHTMLFull\Demo Day URLs.xlsx
Usage:
- Steps to train the model: Put all of the html files to be used in DemoDayHTMLFull. Then run web_demo_features.py to generate the features matrix, training_features.txt. Then, run demo_day_classifier_randforest.py to generate the model, classifier.pkl. Make sure that in demo_day_classifier_randforest.py, USE_CROSS_VALIDATION is set to False in order to generate the model.
- Steps to run: In the file crawl_and_classify.py, set the variables to whatever is wanted. Then, run crawl_and_classify using python3. It will download all of the html files into the directory CrawledHTMLPages, and then it will generate a matrix of features, CrawledHTMLPages\features.txt. It will then run the trained model saved in classifier.pkl to predict whether these pages are demo day pages, and then it will save the results to CrawledHTMLPages\predicted.txt.
Possibly useful programs
Google bindings for python
E:\McNair\Projects\Accelerators\Spring 2017\Google_SiteSearch
PDF to text converter
E:\McNair\Projects\Accelerators\Fall 2017\Code+Final_Data\Utilities\PDF_Ripper
HTML to text converted
E:\McNair\Projects\Accelerators\Fall 2017\Code+Final_Data
Resources
- https://www.tensorflow.org/tutorials/word2vec
- https://machinelearnings.co/tensorflow-text-classification-615198df9231
- http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/
- https://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw
