We develop a pipeline methodology for unsupervised text analysis on Twitter data.
We use off the shelf transformer models, along with techniques for topic selection and point clustering, and easily produce clusters that approximate real world opinions and topics of discussion.
We then analyze these clusters to find some interesting empirical results in the data from this time period.