Workers Auto Approve
First Pass on Data Analysis for ETHPrize Interviews
Note: this issue is reserved for the excellent @jlas :)
**As a** PM managing [ETHPrize](http://ethprize.io/) bounties
**I want to** perform data analysis on the current 82 (and counting!) developer interviews
**So that** I can know with confidence the biggest developer pain points to focus efforts on the right areas for the next bounties.
[ETHPrize](http://ethprize.io/) is a community research and bounty project that identifies the most critical issues facing the Ethereum ecosystem and organizes bounties to solve them. The current two bounties are $250,000 USD each and cover an Ethereum Package Manager and an Open Source Block Explorer.
82 (and counting!) interviews have been conducted in the space to determine where to focus efforts next. It would be great to perform data analysis on these interviews to help focus efforts.
- Perform some type of data analysis on text of the interviews to try to glean insights from them.
> Two approaches I am familiar with are analyzing n-grams and the LDA generative model.
> With analyzing n-grams the idea is to parse the corpus of text and find which groups of words are most common. For example for a 2-gram analysis you would parse the text into groups of 2 words that appear side by side in the text and just count up each of these pairs.
> The LDA model is a really cool graphical model which stipulates that words in a corpus of text are "generated" from a statistical distribution across different "topics". There is a cool library called gensim in python which you can use to load up a corpus and spit out these topics. The interesting things to see is which topics are prevalent, and for each document (I guess a single person's interview text would be a "document" here) which topics it most represents.
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