Sentiment analysis is a powerful tool that allows computers to understand the underlying subjective tone of a piece of writing. This is something that humans have difficulty with, and as you might imagine, it isn’t always so easy for computers, either. But with the right tools and Python, you can use sentiment analysis to better understand the sentiment of a piece of writing.
Why would you want to do that? There are a lot of uses for sentiment analysis, such as understanding how stock traders feel about a particular company by using social media data or aggregating reviews, which you’ll get to do by the end of this tutorial.
In this tutorial, you’ll learn:
- How to use natural language processing (NLP) techniques
- How to use machine learning to determine the sentiment of text
- How to use spaCy to build an NLP pipeline that feeds into a sentiment analysis classifier
This tutorial is ideal for beginning machine learning practitioners who want a project-focused guide to building sentiment analysis pipelines with spaCy.
You should be familiar with basic machine learning techniques like binary classification as well as the concepts behind them, such as training loops, data batches, and weights and biases. If you’re unfamiliar with machine learning, then you can kickstart your journey by learning about logistic regression.
When you’re ready, you can follow along with the examples in this tutorial by downloading the source code from the link below:
Get the Source Code: Click here to get the source code you’ll use to learn about sentiment analysis with natural language processing in this tutorial.
Using Natural Language Processing to Preprocess and Clean Text Data
Any sentiment analysis workflow begins with loading data. But what do you do once the data’s been loaded? You need to process it through a natural language processing pipeline before you can do anything interesting with it.
The necessary steps include (but aren’t limited to) the following:
- Tokenizing sentences to break text down into sentences, words, or other units
- Removing stop words like “if,” “but,” “or,” and so on
- Normalizing words by condensing all forms of a word into a single form