Result of fitness band experiment

As I am progressing with my PhD study I haven't been able to write down any article because I have been busy. And not only PhD made me busy for whole time since January this year, I have to care about work and family.

Nevertheless I should point out results from Introduction fitness band experiment when I wrote about it one and half year ago and never continued.

What was the experiment about

To find relation between sentiment (represented by text recorded in twitter just for practical reasons) and human activity represented by footsteps. Practically about finding link between soft data - sentiment - and hard, measured data.

Reading data: Github implementations

Data and it's processing and analysis code I will publish another time, because I don't have them yet at GitHub.

Twitter data reading implementation

What I have is implementation or reading tweets from Twitter API through tweepy. Which is refactored original version. The reason of refactoring is that I work on second version of fitness band experiment.

Jawbone data reading implementation

What I also have is implementation of reading data from Jawbone API through many different libraries. It's small mess which I need to clean up later when I will use Jawbone again.

Story telling

Long story short

All the data has been extracted, processed and was defined hypothesis which wasn't rejected. Unfortunately, rejection of null hypothesis in favor of alternative was expected and it doesn't happened. That's result and that's the long story short. The whole article presented on HEALTHINF 2016 conference in February this year (2016) is possible to get here:



You can also follow up with the whole story in following chapter, if your are not interested in paper it self right now.