Want to learn how to use Python for Text Mining / Natural Language Processing (NLP)?
This repository has everything that you need to get started!
Author: Ties de Kok (Personal Page)
These materials accompany a PhD session on NLP for Accounting Research: slides
Quick link to the notebook: open notebook
Table of contents
Introduction
The goal of this GitHub page is to provide you with everything you need to get started with Python and Natural Language Processing (NLP)
The following topics are discussed:
(Note: the neural network part is only a reference to the Stanford course CS224n)
Who is this repository for?
The topics and techniques demonstrated in this repository are primarily oriented towards empirical research projects in fields such as Accounting, Finance, Marketing, Political Science, and other Social Sciences.
However, many of the basics are also perfectly applicable if you are looking to use Python for any other type of Data Science!
How to use this repository?
This repository is written to facilitate learning by doing.
All the material is written up in a Jupyter Notebook. See: NLP_notebook.ipynb.
The topics are split up by task description.
It is best to view the notebook locally or on nbviewer using this link: click here
An environment.yml file is provided that you can install using conda, this will automatically install all the packages used in the notebook.
Instructions on how to install the environment are provided here: Install environment
Not yet familiar with the basic Python syntax?
Please check out my "Getting started with Python for Research" repository: click here
Using Jupyter
To run the provided notebook file you need to use Jupyter Lab or Jupyter Notebook.
Jupyter comes pre-installed with the Anaconda distribution so you should have everything already installed and ready to go. The environment.yml will also install Jupyter Lab if you prefer to use that.
What is the Jupyter Notebook?
From the Jupyter website:
The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text.
In other words, the Jupyter Notebook allows you to program Python code straight from your browser!
How does the Jupyter Notebook work in the background?
The diagram below sums up the basics components of Jupyter:
At the heart there is the Jupyter Server that handles everything, the Jupyter Notebook which is accessed and used through your browser, and the kernel that executes the code. We will be focusing on the natively included Python Kernel but Jupyter is language agnostic so you can also use it with other languages/software such as 'R'.
It is worth noting that in most cases you will be running the Jupyter Server on your own computer and will connect to it locally in your browser (i.e. you don't need to be connected to the internet). However, it is also possible to run the Jupyter Server on a different computer, for example a high performance computation server in the cloud, and connect to it over the internet.
How to start a Jupyter Notebook?
The primary method that I would recommend to start a Jupyter Notebook is to use the command line (terminal) directly:
- Open your command prompt / terminal (on Windows I recommend the Anaconda Prompt)
- Activate the environment
conda activate PythonNLPTutorial cd(i.e. Change) to the desired starting directory
for example:cd "C:\Files\Work\Project_1"
Note: if you are changing do folder on another drive you might have to also switch drives by typing, for example,E:- Start the Jupyter Notebook server by typing:
jupyter notebookorjupyter lab
This should automatically open up the corresponding Jupyter Notebook/Lab in your default browser.
You can also manually go to the Jupyter Notebook/Lab by going to localhost:8888 with your browser.
How to close a Jupyter Notebook/Lab server?
If you want to close down the Jupyter Server: open up the command prompt window that runs the server and press CTRL + C twice.
Make sure that you have saved any open Jupyter Notebooks!
How to use the Jupyter Notebook?
Some shortcuts are worth mentioning for reference purposes:
command mode --> enable by pressing esc
edit mode --> enable by pressing enter
command mode |
edit mode |
both modes |
|---|---|---|
Y : cell to code |
Tab : code completion or indent |
Shift-Enter : run cell, select below |
M : cell to markdown |
Shift-Tab : tooltip |
Ctrl-Enter : run cell |
A : insert cell above |
Ctrl-A : select all |
|
B : insert cell below |
Ctrl-Z : undo |
|
X: cut selected cell |
Code along!
Option 1: clone repository
You can essentially "download" the contents of this repository by cloning the repository.
You can do this by clicking "Clone or download" button and then "Download ZIP":
If you extract the downloaded ZIP to a folder you can start the Jupyter Notebook/Lab in that folder and access the notebook.
Environment
You can install the environment by following these steps:
- Make sure you have Anaconda installed (link)
- Open your command prompt / terminal (on Windows I recommend the Anaconda Prompt)
cd(i.e. Change) to the folder where you extracted the ZIP file
for example:cd "C:\Files\Work\Project_1"
Note: if you are changing do folder on another drive you might have to also switch drives by typing, for example,E:- Run the following command
conda env create -f environment.yml - Activate the environment with:
conda activate PythonNLPTutorial
A full list of all the packages used is provided in the environment.yml file.
Option 2: use Binder
Note: some functionality might not work on Binder.
Questions?
If you have questions or experience problems please use the issues tab of this repository.
License
MIT - Ties de Kok - 2020
Special Thanks
https://github.com/teles/array-mixer for having an awesome readme that I used as a template.