How to build a social media sentiment analysis pipeline with scikit-learn

A series of Jupyter notebooks that illustrate how to build a robust sentiment pipeline in scikit-learn. You can see the posts on my blog at https://ryan-cranfill.github.io/sentiment-pipeline-sklearn-1. Our use case here is sentiment classification of Twitter posts.

Requirements

• pandas
• NumPy
• scikit-learn (>= 0.18)
• Twython (for getting Twitter data)
• NTLK
• pandas_confusion (for neato confusion matrices)
• Jupyter (if you want to run the notebooks yourself)

Setup

If you want to fetch the Twitter data locally, you'll need to populate the variables for Twitter API app key and secret, plus a user token and secret at the top the file fetch_twitter_data.py. You can create a Twitter API app here.