Getting Started with Sentiment Analysis using Python

Sentiment analysis is the automated process of tagging data according to their sentiment, such as positive, negative and neutral. Sentiment analysis allows companies to analyze data at scale, detect insights and automate processes.

In the past, sentiment analysis used to be limited to researchers, machine learning engineers or data scientists with experience in natural language processing. However, the AI community has built awesome tools to democratize access to machine learning in recent years. Nowadays, you can use sentiment analysis with a few lines of code and no machine learning experience at all! 🤯

In this guide, you'll learn everything to get started with sentiment analysis using Python, including:

1. What is sentiment analysis?
2. How to use pre-trained sentiment analysis models with Python
3. How to build your own sentiment analysis model
4. How to analyze tweets with sentiment analysis

Let's get started! 🚀

1. What is Sentiment Analysis?

Sentiment analysis is a natural language processing technique that identifies the polarity of a given text. There are different flavors of sentiment analysis, but one of the most widely used techniques labels data into positive, negative and neutral. For example, let's take a look at these tweets mentioning @VerizonSupport:

• "dear @verizonsupport your service is straight 💩 in dallas.. been with y’all over a decade and this is all time low for y’all. i’m talking no internet at all." → Would be tagged as "Negative".

• "@verizonsupport ive sent you a dm" → would be tagged as "Neutral".

• "thanks to michelle et al at @verizonsupport who helped push my no-show-phone problem along. order canceled successfully and ordered this for pickup today at the apple store in the mall." → would be tagged as "Positive".

Sentiment analysis is used in a wide variety of applications, for example:

2. How to Use Pre-trained Sentiment Analysis Models with Python

[{'label': 'POSITIVE', 'score': 0.9998},
 {'label': 'NEGATIVE', 'score': 0.9991}]

Let's dive in!

Output: