https://github.com/gonzaferreiro/NLP_with_20newsgroups

In this brief project we're gonna explore a few NLP tools using a Sklearn dataset and the following modelling techniques: bag of words, Hashing and TF-IDF vectorizer.
https://github.com/gonzaferreiro/NLP_with_20newsgroups

Last synced: 9 days ago
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In this brief project we're gonna explore a few NLP tools using a Sklearn dataset and the following modelling techniques: bag of words, Hashing and TF-IDF vectorizer.

• Host: GitHub
• URL: https://github.com/gonzaferreiro/NLP_with_20newsgroups
• Owner: gonzaferreiro
• Created: 2019-07-08T13:41:19.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-07-08T16:17:11.000Z (over 5 years ago)
• Last Synced: 2024-08-01T13:36:03.118Z (3 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 1.41 MB
• Stars: 10
• Watchers: 0
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Introduction to Natural Lenguage Processin

## introduction

In this brief project we're gonna explore a few NLP tools using a Sklearn dataset. The 20 newsgroups dataset comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for training (or development) and the other one for testing (or for performance evaluation). The split between the train and test set is based upon a messages posted before and after a specific date.

In this project we'll only work trying to predict four categories of the Sklearn dataset:

* alt.atheism

* talk.religion.misc

* comp.graphics

* sci.space

Feel free to check the [dataset documentation](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html) to know more about it. 

## What you'll find in this repository

* Introduction to the dataset and its exploration

* Bag of words model: what it is and application

* Exploring most common words in several ways

* Looking at the confusion matrix out of our model

* Using Hashing and TF-IDF: theoretical introduction and application

* A classifiers comparison