Natural language processing (NLP) is a machine learning technique that breaks down and quantifies unstructured data (human language) so that it can be read automatically by machines. NLP includes all systems that facilitate back-and-forth communication between machines and humans in human language.
NLP is used in text analytics tools, like these free online models below, to gain insights from your data:
The beauty of NLP tools is that they’re able to automate processes in just seconds or minutes, and setting up your own deep learning NLP models in Python is easier than you think. SaaS tools, like MonkeyLearn, allow you to get started with text analysis right away – with very little code.
Forget about using hard-to-install Python environments, like scikit learn and NLTK. Instead, sign up to MonkeyLearn for free and get the most out of NLP techniques and processes, like word tokenization, stemming, and lemmatization, and take advantage of all the real-world uses.
How to Do Natural Language Processing With Python Tutorial
Follow along for a quick and easy setup to start performing NLP with MonkeyLearn’s Python API.
1. Install MonkeyLearn Python SDK
The API tab shows how to integrate using your own Python code (or Ruby, PHP, Node, or Java). For this tutorial, we’ll show you how to do sentiment analysis (a technique that evaluates the opinion expressed in text). The MonkeyLearn API will access NLP models automatically:
MonkeyLearn offers SDKs in a number of languages for super easy integration. But you can also send plain requests to the MonkeyLearn API and parse the JSON responses yourself.
pip install monkeylearn
2. Run your model
Now that you’re set up, enter the following code to start MonkeyLearn’s NLP sentiment analysis:
from monkeylearn import MonkeyLearn
ml = MonkeyLearn('<<Your API key here>>')
data = ['The restaurant was great!', 'The curtains were disgusting']
model_id = 'cl_pi3C7JiL'
result = ml.classifiers.classify(model_id, data)
To try out other models, just switch out the model ID. You can find model IDs from your MonkeyLearn dashboard. Select the model you want, click ‘Run’, then ‘API’. The model ID will appear at the top of the page.
3. Output your model
The output will be a Python dict generated from the JSON sent by MonkeyLearn – in the same order as the input text – and should look something like this:
'text': 'The restaurant was great!',
'text': 'The curtains were disgusting',
It’s that simple. You’re set up to perform all manner of NLP automatically and get real insights from your data. You can see full documentation of our API and its features in our docs.
The Take Home
Now that you’ve learned about NLP sentiment analysis using Python, you can use MonkeyLearn’s APIs to perform other NLP tasks like keyword extraction, topic and language classification, and more.
You can even create a custom sentiment analysis model for free using our simple interface. With MonkeyLearn you can connect tools you use every day, like Excel, Google docs, Zendesk, Zapier, and more.
Sign up to MonkeyLearn and get the most out of your data.
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