Demo Day Page Google Classifier

McNair Project

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. The classifier itself takes:

A: The number of times each word in words.txt occurs in 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, and month words group in seasons. 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.

B: 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

2018-04-09: Wrote the code to put everything together. It runs the google crawler, creates the features matrix from the results, and then runs the classifier on it. This can be used to increase the size of the dataset and improve the accuracy of the classifier.

• 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 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

Demo Day Page Parser

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