Difference between revisions of "Ecosystem Organization Classifier"

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===Text Processing===
 
===Text Processing===
  
There are two possible classification methods for the processing the text of target HTML pages. The first is a "Bag of Words" approach, which uses Term Frequency – Inverse Document Frequency to do basic natural language processing and select words or phrases which have discriminant capabilities. The second is a Word2Vec approach which uses shallow 2 layer neural networks to reduce descriptions to a vector with high discriminant potential. (See "Memo for Evan" in E:\mcnair\Projects\Incubators for further detail.)  
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There are two obvious classification methods for the processing the textual descriptions. The first is a "Bag of Words" approach, which uses Term Frequency – Inverse Document Frequency (TF-IDF) to do basic natural language processing and select words or phrases which have discriminant capabilities. The second is a Word2Vec approach which uses a shallow 2 layer neural network to reduce descriptions to a vector with high discriminant potential. (See "Memo for Evan" in E:\mcnair\Projects\Incubators for further detail.) We are going to be trying both approaches.
  
 
==Related Projects==
 
==Related Projects==

Revision as of 14:56, 30 March 2019


Project
Ecosystem Organization Classifier
Project logo 02.png
Project Information
Has title Ecosystem Organization Classifier
Has start date
Has deadline date
Has project status Active
Is dependent on Crunchbase Database, VentureXpert Database
Does subsume Defining Incubators, Incubator Seed Data, Incubators in Five Ecosystems
Copyright © 2019 edegan.com. All Rights Reserved.


Introduction

The purpose of this project is to build a classifier, which takes the description of an ecosystem organization (i.e., a startup, a venture capitalist, an incubator, etc.) and either correctly classifies the organization's type or correctly classifies incubators vs. non-incubators.

Text Processing

There are two obvious classification methods for the processing the textual descriptions. The first is a "Bag of Words" approach, which uses Term Frequency – Inverse Document Frequency (TF-IDF) to do basic natural language processing and select words or phrases which have discriminant capabilities. The second is a Word2Vec approach which uses a shallow 2 layer neural network to reduce descriptions to a vector with high discriminant potential. (See "Memo for Evan" in E:\mcnair\Projects\Incubators for further detail.) We are going to be trying both approaches.

Related Projects

Subsumed Projects: Defining Incubators, Incubator Seed Data, Incubators in Five Ecosystems

This project is dependent on: Crunchbase Database, VentureXpert Database