Introduction

Since I learned that analytics could be applied to television I knew that’s what I wanted to do, but I quickly learned interesting data about television is hard to acquire for a nonprofessional television and analytics enthusiast. A few months ago I got put on a project at work pulling and analyzing tweets for a company around the same time as speculation started about who was going to be the next bachelor. It finally occurred to me that I had access to a huge repository of data about television, social media, twitter in particular.

I wanted to know what people were actually saying about the next bachelor and break it down a little more than the one sided view I was getting on the internet. At that time people were mostly tweeting about Bachelor in Paradise with the #TheBachelor hashtag and with the Twitter API limits if I just used the standard hashtag I would get a very small number of tweets about the next Bachelor. Fortunately Bachelor creator Mike Fleiss started tweeting clues about the next bachelor, so I pulled all of the tweets in response to his clues, did some simple sentiment analysis, and came up with this:

bachelor_pres

bachelor_pres2

Not exactly earth shattering stuff, I didn’t even standardize the axes, but I had a lot of fun doing it. I currently work in a consulting type role, so the data I analyze is from a variety of industries. I never get the opportunity to be a subject matter expert in any of the data that I work with. I would consider myself a Bachelor Franchise subject matter expert, and it was exciting to actually go into a data set and know what I was looking at right away. I loved it and I wanted to do more of it, this time with bigger data sets and more analysis.