Listing Page Classifier

Project

Listing Page Classifier
Project Information
Has title	Listing Page Classifier
Has owner	Nancy Yu
Has start date
Has deadline date
Has project status	Active
Copyright © 2019 edegan.com. All Rights Reserved.

Summary

The objective of this project is to determine which web page on an incubator's website contains the client company listing.

The project will ultimately use data (incubator names and URLs) identified using the Ecosystem Organization Classifier (perhaps in conjunction with an additional website finder tool, if the Incubator Seed Data source does not contain URLs). Initially, however, we are using accelerator websites taken from the master file from the U.S. Seed Accelerators project.

We are building three tools: a site map generator, a web page screenshot tool, and an image classifier. Then, given an incubator URL, we will find and generate (standardized size) screenshots of every web page on the website, code which page is the client listing page, and use the images and the coding to train our classifier. We currently plan to build the classifier using a convolutional neural network (CNN), as these are particularly effective at image classification.

Current Work

Main Tasks

1. Build a site map generator: output every internal link of input websites
2. Build a tool that captures a screenshot of individual web pages
3. Build a CNN classifier using Python and TensorFlow

Approaches (IN PROGRESS)

Progress Log (updated on 4/15/2019)

Site Map Generator

Performing 2 following algorithms to find all internal links of a web page with 2 given user inputs: homepage url and depth

• BFS approach

E:\projects\listing page identifier\Internal_Link\Internal_url_BFS.py

• DFS approach

E:\projects\listing page identifier\Internal_Link\Internal_url_DFS.py

Web Page Screenshot Tool (IN PROGRESS)

E:\projects\listing page identifier\screen_shot\screen_shot_tool.py

Image Processing

This method would likely rely on a convolutional neural network (CNN) to classify HTML elements present in web page screenshots. Implementation could be achieved by combining the VGG16 model or ResNet architecture with batch normalization to increase accuracy in this context.