Has anyone really been far even as decided to use even go want to look more like?

You read that right.

The intersection of text analytics, social analytics, and neuroanalytics is incredibly interesting and useful.

It’s an old meme, and you read about it’s origin here.

“Has anyone really been far even as decided to use even go want to look more like?” is a 4Chan-ism. I suspect that it was written by a linguist. Awesome troll is awesome. It can be roughly translated to:

“Has anyone really decided as to even go that far in wanting to do to look more like so?”

Subject – Anyone Verb – decided (modified by “really” adverb) Direct object to “decided” – “that” (pronoun modified by “far”) Noun clause that clarifies “that” – “wanting to do” (gerund phrase) Do what? – “to look more like so”

Meaning:

“Has any video game company really taken such measure to make a game so realistic?”

This is the kind of stuff that if a human has a hard time interpreting, a machine is going to have especially hard time codifying and returning some sort of valid output.

This all goes beyond just identifying words in a stream of text and trying to assign some sort of value to them. To be sure, volumetric measurement of mentions is an important first step. Yet, buried in words is what a person is like, how they’re feeling, and what they intend the reader to feel.

Copywriters know how to write for a Grade 5 reading level, a Grade 10 writing level, and a university reading level based on a relatively simple algorithm. It follows that since words are machine readable, they can be treated very similarly to numerical input.

Take, for instance, the sequence of words:

“Butterfly violet breeze fizzy”

and:

“Papilio #800080 easy sparkling”

They each individually mean the same thing. Of course, they don’t emotionally mean the same thing. The words have different shapes. The speaker of the former would be a normal person, maybe trying to write some poetry. The latter would be some sort of biologist programmer.

Words could be broadly categorized into different buckets, with great analytical effect. But it goes beyond just words. Verbs are where it starts to really get tricky.

If you want to really torture yourself, try reading “Investigations in Universal Grammar” and “The Stuff of Thought” in the same week. Take this quote from page 66 of “The Stuff of Thought”:

“Some intransitive verbs resist the intrusions of a causal agent:

The bay is crying.
The thunder is crying the baby.

The frogs perished.
Olga perished the frogs.

My son came home early.
I came my son home early.

And some transitive verbs resist the attempt to strip their causal agents away:

We’ve created a monster!
A monster has created!

She thumped the log.
The log thumped.

He wrecked the car.
The car wrecked.”

It’s fairly hard to teach a machine how to interpret things that humans can hardly interpret themselves. Or grammatical rules that only seem to make real sense to the mother tongues’ ears.

It’s worth figuring out and applying.

Take landing page copy:

The purpose of a landing page, to a direct marketer at least, is to get the person to convert: to take a desired action. Good copywriters know how to use words and tone to compell people to continue reading down the page, like a slide. The theory is that if their head starts nodding at the top, they’ll slide down the page, they’ll continue saying ‘yes’ right into a sale. The copy, ideally, should ressonate with who the customer is intended.

I’m fairly certain that certain classes of words are better and convert more than other classes of words. Beyond that though, certain classes of verbs and tones are better at converting than others, in different contexts. The answers could mean the difference between 5% conversion and 20% conversion.

This is one of the thrusts with sentiment analysis. Useful and relevant.