Build a Brand Sentiment Analysis Extension for Twitter
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Learn how to build a brand sentiment analysis extension for Twitter. Use natural language processing and machine learning to analyze Twitter data, gauge public sentiment around a brand, and create actionable insights for marketing and branding strategies.
At a Glance
Are you a Twitter enthusiast who wants to quickly see the tweets’ sentiment on your timeline? Or maybe you’re an organization and want to understand the sentiment behind your brand. Whatever the reason, this lab will show you how to create a simple browser extension that performs sentiment analysis on Twitter posts and displays the results in an easy-to-consume format.
Here’s a quick demo of the extension in action after searching for Spotify!
A Look at the Project Ahead
- Learn about browser extensions and their purpose.
- Explore the concept of sentiment analysis and its applications.
- Use IBM Watson’s pre-trained natural language processing (NLP) model to analyze text data for the sentiment.
- Use Chart.js to create and display a pie chart in a browser extension.
- Test and debug your browser extension to ensure it functions properly.
- (Optional) Create and deploy your browser extension to the add-on/extension market.
- Host the sentiment model for free* on IBM Code Engine
*A credit card is required but you won’t be charged until you reach a certain usage.
What You’ll Need
If you would like to deploy your Sentiment analysis model to IBM Cloud Code Engine which is an optional step. In that case, you’ll need to provide a credit card to your IBM Cloud account associated with your IBMid to allow the Code Engine commands to run. But you won’t be charged until you reach a certain usage.
If you would like to deploy the extension to the Firefox Add-ons Developer Hub for others to use, you will need to be registered as a Developer on their Hub. This is entirely optional and you are able to use your created extension locally without being registered.
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