Last week we pulled everything that was said about banks on social media (Twitter, Facebook, and Instagram) and ran it through a text analytics engine to see what we could learn. This was over 1,000 posts, largely retail-focused, where we looked at word choice, subject, sentiment, style and tendencies. Our takeaway lessons are distilled below and some of the findings may help you be more proactive in heading off potential problems.
Anger
In a moderate corner of the big data world, lies a discipline called text analytics. Text analysis is the mining of data contained in natural language done to derive bias, sentiment, sentence structure and word usage. If you have seen a “word cloud” you have seen the basic building block of text analysis. While banks generate a lot of numbers, we generate almost as much narrative from such documents like new product approvals, customer surveys, legal agreements and mostly, credit memorandums created to document the underwriting on a loan.
