#4: Mine Twitter Data

#4: Mine Twitter Data

Thanks to the Internet—and, increasingly, the Internet of Things—we now have access to hordes of data that weren’t available even a decade ago. Analytics is a huge part of any field that works with data. What are people talking about? What patterns can we see in their behavior?

Twitter is a great place to get answers to some of these questions. If you’re interested in data analysis, then a Twitter data mining project is a great way to use your Python skills to answer questions about the world around you.

Our Twitter sentiment analysis tutorial will teach you how to mine Twitter data and analyze user sentiment with a docker environment. You’ll learn how to register an application with Twitter, which you’ll need to do in order to access their streaming API.

You’ll see how to use Tweepy to filter which tweets you want to pull, TextBlob to calculate the sentiment of those tweets, Elasticsearch to analyze their content, and Kibana to visualize the results. After you finish this tutorial, you should be ready to dive into other projects that use Python for text processing and speech recognition.