Natural language processing is an incredibly useful application of machine learning.
Natural language processing using python is making this powerful technique increasingly accessible.
With the support of some simple to implement libraries, you can now start using natural language processing with python in just a few steps.
But why would you want to use natural language processing?
When is Natural Language Processing used by machine learning engineers?
Natural language processing techniques are used by machine learning engineers to interpret
Essentially you are teaching a computer to read using machine learning.
Once processed the text can then be fed into other machine learning algorithms to gain insights from it.
Natural language processing can, therefore, play a role in multiple machine learning problems.
When you combine powerful natural language processing with python, then you have even more opportunities.
How do you start natural language processing with python?
Before you can even begin to start using natural language processing with python you first need to clean your data.
This process is called data pre-processing and is essential for ensuring you get a good output from your algorithm.
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Let’s be clear, cleaning the data is vital for all machine learning algorithms but there is a unique process for natural language processing.
Why is cleaning the data so important for natural language processing?
First of all, It is important that you remove all punctuation from the dataset. This is part of the process of cleaning the dataset.
The step of removing the punctuation is necessary for natural language processing to minimize the variation in words in the dataset.
There are several different types of words and characters which can also be removed from the dataset. This all helps to minimize the number of words,
The minimization of the number of words in the dataset is essential to help speed up processing.
What are the other steps involved in cleaning the data?
- Remove capitals – making everything lowercase
- Removing stop words
- Stemming – taking each word back to its root
Creating a bag of words model using natural language processing
You’ve cleaned the data – fantastic!
Now we can get onto the fun stuff – creating a bag of words based model!
Now you have a dataset, what do you do with it?
Once you have used natural language processing to get the corpus dataset, now you can feed this into a machine learning model.
There are two types of machine learning model we will discuss here.
These are listed below:
- Clustering algorithms: You can use clustering algorithms to find patterns in the data such as themes from the words used. This is a technique that is used to identify fake news. You can read about the used in fake news here. Alternatively, if you want to start implementing clustering algorithms on your dataset, check out this article.
- Classification algorithms: If you have a dataset from which you want to be able to classify a result from the language used, for example, reviews data, you can use classification algorithms. Using these algorithms, you can analyze the language used to predict whether a review is good or bad.
How do you implement natural language processing using python?
In this section, I have provided links to the documentation in Scikit-Learn for implementing natural language processing in python
Implementing natural language processing with python using if statements, natural language processing and the Scikit-Learn modules.
Below is a list of resources you can use to start:
- Cleaning the data – the screenshot above gives examples of how to clean the data
- Stemming – using the
nltk library you can reduce words to their stem - Classification algorithms – check this article for an overview of some of your options.
Wait a sec, in the documentation, it says you can clean the data directly using the … module.
Why am I bothering to clean the data manually?
Why should you manually clean the data for natural language processing with python?
There is one fundamental reason why you should manually clean the data, quality control.
Though you can clean the data directly using the …., it is better to do it manually.
The reason why manually cleaning the data is so important is that you have control over the steps taken. This helps you check all the information is correctly processed before it enters the model.
Also, you can complete more actions to better clean the data by doing this manually.
I know I keep saying ‘manually’ like you are doing it yourself.
You’re still using python – stop being so lazy! ;P
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5 Exciting applications of natural language processing!
To close, here are a few exciting examples of how natural language processing is used:
- Fake news identification – by clustering groups of words to find fake news articles
- Voice assistants – assistants like Alexa, use natural language processing to understand what you need from them
- Voice to text applications – convert your speech into a text message with ease
- Understanding sentiment – do they like it or hate it? Natural language processing will help you find out
- Automated summarisation – are you bored of reading through long articles? Natural language processing can help you do this by auto-summation.
Enjoy using this powerful tool!
Want to learn more about artificial intelligence? Check out this guide on everything you need to know.
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If you have found this content helpful, I recommend the course linked below which gave me a baseline understanding of the materials and python code shared here.
Machine Learning A-Z: Hands-On Python & R In Data Science
