Saturday, September 12, 2015

#ksleg Power Rankings September 12th

I spent last week on vacation out of state, not watching news reports or reading local papers, so I'm fairly clueless about what happened in Kansas while I was gone.  Is there a better way to remedy that than another out-of-session round of #ksleg power rankings and topic mining?  Probably.  Is there a funner/nerdier way? Probably not.

TOPIC MODELING

So first the most important (sarcasm) part.  A wordcloud.


Not too much surprising there, we are generally seeing the normal top words, like court, teacher, school, and fund.  On to the topic mining, what topics did our topic mining algorithm find (correlated topic models)?


The model found five individual topics within the data.  Here's what the tweets most associated with those topics referenced:
So that was a quick rundown of topics, and by quickly googling around, these appear to cover the biggest stories in Kansas over the past seven days.

SENTIMENT MINING

Interesting topics, but what about how people feel? Our sentiment mining algorithm helps with that.   First we mined the general polarity of tweets, and found that the tweets were slightly more negative than the last time we looked.  Specifically, whereas in late August the negative:positive ratio was 2:3, it is now approximately 3:4.

We can also award the @MichaelofAustin memorial award for the most emotionally-negative reply tweets.  These are the accounts most often replied to with the emotions of fear,sadness, and anger.  .  These are effectively, this week's most hated #ksleg accounts, per our sentiment mining algorithm.  Looks like the governor caught the most negativity.  (I was going to make this a top three list, but when jrclaeys, who I attended high school with, popped up at number four, I decided to expand)

POWER RANKINGS

And now, the moment that no one really cares about, our #ksleg power rankings.  This time, I went to the trouble of actually developing a working index, based on tweets, retweets, and favorites that correlates to actual reach.  The top tweeter is indexed to 1.000, and other tweeters get scores that are effectively ratios of that.  Ratios between tweeters in this index are also meaningful, whereas a tweeter with a .400 index would have roughly double the reach of one with a .200 index. 


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