How Good Things Are
The main purpose of Cohaico is to let you quickly see what the whole twittersphere thinks about a particular movie or gadget.
At First
We look at all tweets and extract the ones that contain a reference to a particular object (e.g. "Moneyball") and then we show those tweets to you.
Alas, there are many people on Twitter and lots of them have something to say about Moneyball. So we started looking at all of these tweets and showing you only a select few, which we think are the most interesting ones. And if you sign in with your Twitter account - we show you what your friends said first.
We also showed how many mentions are there for a particular object - so you can tell how "popular" it is.
That's not good enough either. Many times you just want to see a bottom line of what do "people in general think" about something.
So Later
We started analyzing the sentiment within each tweet: how "positive" or "negative" it is.
Example:
"I just watched Moneyball and it's a masterpiece. I loved every moment."
Positive.
"I just watched Moneyball and it sucks bananas. I'd rather have my hair picked one by one than watch this crap".
Negative.
And what we did after analyzing sentiment is show you how many of the total mentions for Moneyball are positive.
So know you could (in theory) know how good Moneyball is according to Twitter.
The problem with this turned out to be two-fold.
Because different things have different numbers of mentions, it was hard to compare one thing against another.
Say you have "Moneyball" with 1,200 positive out of 8,000 and "Real Steel" with 2,880 positive out of 17,500. Yeah if you're good with math you can compare those two and figure out that Real Steel is slightly more "positive", but it's not that easy.
Also, just by looking at one object, it's really tough to decide wether 1,200 positive out of 8,000 is good or bad.
So, we could have just shown it in percentage, right?
Wrong. The other problem was this: turns out that in addition to tweeting positive and negative things, people also tend to say things that are... neutral.
Example:
Going to watch Moneyball with my BFFs oogie and shoogie.
And not only that, turns out that most tweets are neutral.
So using percentage to display how good something is was tricky. You may take something like Moneyball having 1,200 out of 8,000 and show
Moneyball = 15% positive
But people are used to certain scales when it comes to "quality / ratings", these scales are usually 1-10, 1-100, 1-5 etc.
So something like "15%" sounds pretty bad. But in reality, if you look at all movies on Twitter, it turns out that 15% positive out of all tweets is very good.
So what did we do?
What we did was take all of these percetages for all the objects in a particular vertical (say "movies") and "normalized" them on a 1-100 scale. So the movie having the highest percent of positive mentions (say 40%) will be "100" and the movie having the lowest percent will be "1".
And using this normalized scale we now show how positive a particular object is:
We had the creative department work the weekend, and they came with our term for it - "positivity score".One more thing
But showing a normalized score on a 1-100 scale is not enough still. What you also may want to know is how is this object positioned against others in the same category. That is why, we also show the position in category (e.g. "#2 in movies").
This quickly gives a complete "bottom line" for something, based on the things people tweeted about it.
Clicking the position in category line, leads you to the "most recommended" view for this category, to let you explore further, and find more great movies and gadgets.




