Welcome to Cannabis Data Science right before July 4th.
So we'll keep it light, we'll keep it fun, and then I think we'll walk away at the end
of the day with a few insights because I think the data is so rich that every time we go
out panning, we come back with gold.
So sure enough, got a couple gold nuggets today to share with you and point you in the
direction of some rich gold veins so you can go mining for gold of your own.
Till we jump into this, if any of you have anything to share, so I know it was a long
day for you Jerry, anything on your mind as far as future directions for the group?
So next month is a new month, a new quarter, so perhaps we can turn a new leaf and see
a new fiscal year for some people.
Exactly.
So we can see what new topics may be.
I posted a pretty extensive business model on Slack.
I had a conversation with Charles about it.
I'd like to have one-on-ones with everybody just to see what their thinking is on what
I'm proposing.
I do have to say right out front that I am 100% opposed to getting involved in NFTs or
crypto in any way, form, or any way, shape, or form, and I just won't do it.
I will not be involved in that.
It's a very unstable foundation to build on.
Well to each their own, so for those who are new, what is the crypto, the NFTs, essentially
I was trying to find a mechanism that people can get paid directly for their data and their
algorithms.
Essentially, the downside of analytics is I'm essentially a middleman.
If people want to list their data through their algorithms, they'll have to essentially
contract with analytics.
We all have to get those listed through a mechanism like one of your traditional payment
providers, and it's just a lot of hassle.
What ends up happening is I'll list my data and I'll list my algorithms, but then I don't
think other people get to participate.
The idea is what if people could somehow post their data and then their data remains private
unless it gets purchased by a particularly interested party.
In my exploration of what is going on in the data space, there's a cool company, Ocean
Protocol.
It looks like they've set up a data market.
Like I said, I'm not necessarily sold on it, but it just looks like a way that you could
post a dataset.
I believe it would be encrypted, so I don't think anyone would have access to it unless
they essentially buy from you.
I think it's still the early stages for this technology.
I just see the way the tide is turning.
I don't know if these giant middlemen have much of a long future.
Do all these private companies where the executives walk away at the end of the day with a lot
of money and then maybe they're using interns, not paying the interns, who knows what's going
on?
Is that a sustainable business model?
It's been sustained for hundreds of years.
Exactly.
That's the point is we've had to put up with this for hundreds of years and thank God we
now finally have technologies that may finally allow-
There's cutting edge and there's bleeding edge.
You're on the bleeding edge.
True.
I tell you, the amount of theft, crypto is for criminals.
What you're talking about is a totally unregulated system in which you have no protection whatsoever.
Having been burned, having had identities theft occurred by my involvement in crypto,
the downside is unacceptable.
I'll just push back in that as a small business owner, I already feel like I'm 100% unrepresentative.
It's not like I even have any legal mechanism.
The court systems are so biased.
It's basically money talks.
I've gone through the wringer and I've seen under the curtain, money talks, especially
in that realm.
The problem with crypto is that there's nobody to talk to.
I don't want to go on too much.
I have a problem with my bank.
I go to my banker.
Like I said, if this is something like you're interested in checkout ocean protocol, I'm
not necessarily tying analytics to the crypto space at all.
However, I do explore cool new technologies.
It's something to explore, but I'm not going to put effort into a business that's paying
me in a mechanism that I have no faith in.
Then don't.
Okay.
I'll see you later.
Okay.
That's sort of the way it is.
That's sort of my attitude now is I'm seeing the industry getting captured by big players.
We'll actually talk specifically about that today.
As I said, I'm not even really here to talk about cryptocurrency.
I'm here to talk about statistics.
Why don't we change gears?
That's what I know at the end of the day.
That's what we're in the business of doing.
We aggregate cannabis data and provide statistics as a service.
I was looking at a mechanism for other people to get paid.
That's the way I currently do this.
I actually kind of like that it's controversial because I don't think if you're not stepping
on toes, then I don't think you're making progress.
I think Canlytics does an awesome job at lighting people's pants on fire.
I'm proud for that and going to keep paving the way forward.
I think we're in good territory.
I'll go ahead and share my screen and you two will be in for a couple golden nuggets.
I saw an article the other week where there was a cannabis company.
So where does the story begin here?
So there's Respect My Region, which is a...
I don't want to miscategorize them, but I guess they're like a culture, branding, marketing
company out of Seattle, Washington, I do believe.
Hey, John, we're off to a rocky start, but we're going to just keep paving the way forward
here.
But long story short.
Okay, there's noise, there's company Jungle Boys coming into...
Okay, so what was controversial about the Jungle Boys before I go further?
If I may ask.
Just over on the East Coast, there's a really big push against MSO taking over all of these
smaller licenses that were originally awarded to smaller companies.
Just like from a consumer standpoint, you've got dwindling choices whenever you've got
fewer and fewer companies running the market.
Well, Kimberly, hopefully you'll stay to the end because I do have something to say to
that.
So you kind of, I guess, cut straight to the point there.
My apologies.
Well, it's not that...
No apologies there, but if you're all on board, let me go through...
Well, yeah, we'll save it for the end here because it's sort of a long story to tell
here.
Okay, so it's controversial.
So we've got a company from the West Coast entering into the Florida market.
Okay, so please stay tuned to the end because we'll kind of get to that.
So I'm going to sort of walk through the story.
So we'll kind of keep that in the back of our mind.
Now we'll sort of just enter into the realm of statistics.
We'll touch on statistics here for a little while and then sort of circle back with our
story.
So essentially...
All right, now...
Okay, so cannabis industry...
Okay, not great.
Now we run into the machine learning and also not necessarily great.
You see, okay, you've got people and what you see a lot is people are kind of doing
away with the statistics that underlies all this and we may kind of need to get back to
the theory.
So three big problems and these may even kind of even be similar overlapping problems with
the cannabis industry in its own way.
So three big problems.
So dependency.
So depending on your data, you may get an entirely different outcome.
Consistency.
Depending on how you go about training your models, you may get an entirely different
outcome.
Finally, transparency.
Does anybody even really know what's going on?
I see a lot of hand waving.
I see people just saying, oh, we're going to use model X and then I look into it and
maybe the model is appropriate but it should be standing on some theoretical foundations
here.
Cool.
Sorry, I'm sort of all over the map but I'll tie it all together.
Now, okay, so now where are we going with this?
Okay, so we've got a lot of reviews data and we could actually use machine learning to
get a measure of, for example, people's emotion.
How do people feel about Jungle Boys?
How engaged are people with Jungle Boys?
When they write about Jungle Boys, are they depressed or not?
Does this correlate with someone's personality?
How intense are they about this?
So for example, I just learned about this company honestly just in the past couple of
weeks so my intensity may not be the same as someone who's known about the company for
a longer time.
Firstly, polarity, positively or negatively.
Do you have a negative view of Jungle Boys or a super positive view of Jungle Boys?
So essentially, these are all metrics that we can start to sort of tie into this concept.
So basically, where was I going with this?
Sorry, I got a little frazzled at the beginning but I should be able to bring it all together.
Essentially, Respect My Region, they focus on culture and they say, oh, you know, culture
is what matters.
And what I say is what's cool is when we can tie the data into the culture, right?
So instead of just going off of a narrative, so to speak, we could actually tie in some
data with our narrative.
So just going to show you the data real quick because it is cannabis data science after
all and then we'll, as I said, we'll tie it back into the cannabis industry here at
the end.
Okay, so now that we're to the data, hopefully we can move through a little less frazzled
here.
Also, I may skip this little bit here at the beginning on personality but that's an aspect
that I think we should look at in upcoming weeks is essentially the idea is different
personality types respond differently to different stimuli.
And so I was thinking cannabinoids are in fact stimuli so it would just be interesting
to wonder if different personality types respond differently to different cannabinoids or terpenes,
what have you.
So that's sort of theoretical but something that can be done.
The main thing I wanted to share with you today was these eight metrics that would be
awesome to start collecting from reviews.
And so while this is loading, I'll just share with you the work that can be done.
And so we don't have to necessarily repeat ourselves, right?
We don't want to reinvent the wheel each time.
I was actually thinking about this analogy more.
We just want to put some really cool wheels on a skateboard and go do some tricks.
If the analogy is funny to you or not.
But the long story short is there's a really cool company out there, Cintiq, so they have
an API.
I just emailed them to get an API key and they were pretty quick with the response.
The API keys of course need to be kept private.
I'm still reading their terms of service.
So please feel free to read the terms of service yourself and apply for an API key if you wish.
And then they have these metrics nicely packaged behind API endpoints.
And so whether we can utilize theirs or they also have open sourced all of their code.
So I was maybe going to see if we can't sort of like mix and match or just get all of the
best parts and package them behind our own API.
But the idea is and please check out their work because they've done really rigorous
theoretical work, statistical work backing up their algorithms.
So I think this is a really good starting point.
But we don't necessarily just want to use this as a black box.
So we don't necessarily want to use these as a black box.
So over the I was going to try to do them all in one go but I was a little ambitious.
But we can at least get through these first two.
The idea is instead of just using their algorithm as a black box, which we can definitely do
in production, it would be nice to kind of pick these apart to see what is going on under
the hood.
So we'll actually do that just right now.
So we can go ahead and read in all of these strain reviews that we may have.
And then the idea is there's a couple ways that we can go about grouping these.
So for starters, if you were interested in getting a user profile, you could aggregate
all of the reviews by user.
And so I believe we began to look at that.
But I just wanted to point out a little insight that we can find.
And then I'll show you two new applications afterwards.
So just part in the data loading.
And basically the two other applications are we can also look at strains.
And then finally, we'll look at brands, right?
Because that was sort of where we entered the space was, you know, what's essentially
the sentiment around trangle boards.
And we'll just try to look at the data and then tie it in with the narrative.
This is taking a hot minute.
So if there's any other thoughts, comments, questions, I'd be happy to hear them.
But then we can pay forward.
From Kimmy, as you move forward using this data, I think something to really consider
and something that I'm sure you probably already have is that there's a huge difference from
state to state in all of these metrics.
And every state uses different metrics to decide what products to make and how their
patients are feeling about it.
And just I think it's really important that any analysis that's done is done on, you know,
like a sample of a certain state's data as opposed to a national sample.
I definitely agree with you.
And it's pretty easy to find the state variances, right?
They vary on many different metrics.
So you're 100% correct.
I guess the two things I would say is one, I guess, please just, I guess, acknowledge
the shortcomings of this.
So please take everything as a grain of salt.
Hopefully the statistics can still be reused, right?
You can still use the same statistics with your better data.
So that's sort of the idea is, you know, the statistical models can be used as tools, but
they're only as good, your outputs is only as good as the data.
So we'll just keep in mind that this data is, we'll just assume it's the worst.
So let's not weigh in too, too much.
Or let's just not really weigh our results at all.
So this is more just a demonstration of the statistics versus trying to draw conclusions,
what we're doing today.
And then second, I think, so I think that's all I think I have to say, other than I think
you're spot on in that if you did have a review data set, and you knew where the reviews were
coming from, so you knew like sort of the user location, so you knew like state by state,
then that would be incredibly interesting.
And then, so for example, we're about to do sentiment analysis.
And so it'd be interesting to do a sentiment analysis of say, jungle boys, state by state,
so that way you could actually kind of parse out if sentiment's different than Florida
versus on the West Coast.
That's actually a brilliant thought that you brought up.
So as I actually like to, I kind of realized this and kind of want to drive this home is
all of your questions, comments, thoughts, ideas, these all help move the ball forward.
So you know, I may be punching keys, but that's only part of the picture.
So you can't complete the puzzle without every piece.
And so all the questions, all the feedback, all the listening ears, those are all critical
pieces.
Okay, cool.
So without droning on, let's just go ahead and get into it because I think the narrative
here is almost more interesting than the data, but I'll at least show you how the statistics
is done.
But long story short, you can aggregate all the reviews for all the different users, some
of them are anonymous.
So you know, luckily, they've used pretty, I guess, anonymous names.
Right, we don't really want to be dealing with actual people's names here.
But I guess we'll exclude the anonymous ones and just do a little bit of cleaning up on,
maybe just do a little bit of cleaning up on the reviews.
And basically, what I've done here is basically for each user, I've just taken five random
reviews.
So the idea is, you can kind of see, start to parse out if there's any differences by
user, right?
And so the idea is, if every user is exactly the same, it's probably not going to be meaningful
to do statistics with.
And so that's something a professor told me is, right, you kind of want to look for variants,
right?
So the more variants you see, the more applicable statistics are.
So we can see, okay, this is pretty simple, right?
Different users use different amounts of words.
And so the idea is, you can start to find little intricacies like this.
And then it's essentially many of these facets that are being used in these AI models.
So I was looking at their paper, and they basically just use hundreds of metrics like
this.
So they just use things like word count, just everything under the sun, sentence length,
grammar, word choice.
So they just use all of these metrics.
And then they have a training data set.
So they have someone who wrote an essay and took a personality test.
And then they just basically fit a really, really good regression model.
So they just basically try a bunch of these factors and just see how this helps predict
personality.
So that's sort of what's going on under the hood is, not only do they look at word count,
but this is just basically a metric, and then this is used for prediction purposes.
But before we get to the abstract, let's look at a few more concrete things we can do with
this.
So the idea is, okay, well, you can take out...
So these are these people's essays.
So you can take out all of the filler words, so all the this, the, a, is...
And then what you're left with are essentially all the words that different users chose to
use.
And this is, once again, quite fruitful because...
So this is, I think, kind of where the personality comes from is, right?
There's sort of a correlation between the words people use and perhaps the personality.
But that's sort of rushing ahead way to the end.
The idea now is, what's just sort of the positivity?
So you can kind of see...
Question?
Hey, Keegan, are these essays all for this, written for the same prompts?
Well, the training data set, you mean?
Well, the word count comes from essays, right?
What's the prompt for the essays?
In this case, my essay are strain reviews.
So I just compiled five strain reviews.
So these aren't actual essays.
I'm just more thinking about this is like a corpus of somebody's work.
So people are writing a page and a half with your work of strain review?
That's pretty amazing to me.
I mean, some people like to talk.
Geez.
Actually, we'll actually have those exact statistics here in one second.
And so the idea is perhaps if somebody's writing longer reviews about your strain, that may
be a good sign.
But we can start to tie it all together.
But here, I'll hurry up and get to the strains since I think maybe the more interesting part
here.
But basically, the one little nugget here that I found was, OK, so just to kind of show
you how this works, you can kind of just do a polarity score of different words.
The best is like, so best is positive.
And then I think the idea is the very best.
The algorithm can essentially kind of determine that the very best is more positive than the
best.
And the very worst.
Let's see if they can handle that.
And so then, oh, this is the very worst.
OK, you get a negative score.
So once again, we don't want to reinvent the wheel.
So this is a point where we're going to stand on the shoulders of giants.
We're going to essentially have to assume that the people who've done this work on the
polarity scores did a rigorous job.
And from briefly looking under the hood, it looks pretty rigorous.
It looks like there was...
I don't want to speak too much about it, but essentially it looks like a lot of psychologists
have sort of been working on this.
But once again, if this is something that you're particularly interested in, please
poke under the hood at that point.
Is there a question?
Yeah, hi.
Not to go too deep under the hood, but doesn't context matter when writing?
I'm sorry, doesn't context matter with the polarity scores?
Can you speak a little more to that?
In case you use...
Like it's not the very worst?
Yeah, I'm not really...
I mean, from the keywords you had selected, it looks like classic, muddy, other words,
they could be used in different contexts to give a different score, that I would believe.
Is that not something that could change the data at least?
It sounds like that lack of specificity could be adding noise to your data, Emmanuel.
I'm not sure if that's what you were trying to say.
Yeah, is that something that we should be giving it a like?
If I'm interpreting this correctly, the way I would...
Sorry, basically saying a user may use the word gas to mean good, but that's not captured
in the data.
Exactly.
And so I think this is where you can basically enhance the model.
So I do believe maybe...
You may need to dig into the documentation, but I would like to believe that this is sort
of the baseline.
I think it's just using a baseline word mapping that's pretty basic, like maybe 16,000 words
where they've got best and good and all the real rudimentary words.
And I think you could essentially improve upon it somehow if you had your own mapping
between gas is a good word in the cannabis space.
If I may interject?
I think in trying to get that more specific sample, that's when you would be really digging
in deeper to see what words mean what.
I think that's where you're going to be getting that context.
You're going to have to look at each sample differently because some people might be using
the word gas.
Some people may be using the word fire.
A word for bad out here on the East Coast is boof.
I don't know if people use that.
So it's something that I think you'll really have to get into when you're looking at your
data set.
And then you're just going to have to dig into the data and see what words you're actually
seeing.
Spot on.
And I may have to refer to the prior week or I'll have to share some code with you because
you can find the most frequent words that are used.
So a strategy would be, okay, what words are people frequently using?
And then you may want to go and kind of code those positive or negative.
And then see if you can't supplement your algorithm.
So that way if people are using the word fire, gas, or lit, or what have you, some of this
slang in the cannabis space, it may be worth your while to hand code 100 words or what
have you.
But I guess it's just sort of a cost benefit thing.
You can always keep making your model better, but I guess there's only a marginal benefit
and there will be a marginal cost.
Hey, Keegan.
Please.
Along this line, why don't you quantify a couple of words that are recognized kind of
culturally?
And yeah, boof is an interesting word.
It may, I think it typifies kind of not so good attenuated cannabis.
It's going to be tough maybe to link that into the strain reviews and the content.
But words like dank, words like gas could easily correlate with strains.
We kind of know what that is.
So if you simply did a word, a review count that includes words like dank and gas and
see if it maps to what we know to be the strains associated with that, that would be an interesting
initial validation of this approach.
I think we can do it pretty quickly here.
Let's just do it.
And in fact, you should maybe do dank versus gas and see what's used more.
I think at that point we would be answering a different question, but I'm all with it.
So maybe the question is slightly different, but I'm trying to approach how to validate
a language processing on a set of reviews.
And I go immediately to do strain specific review words like what we just mentioned,
correlate with strains that we know would be in that bucket.
I hope that's clear.
I think you're on to something, John, and this is what I mean by every time you go panning
for gold, you almost can't help but walk away with something.
So I think you're right.
I think if you essentially looked at the most frequent words, you've kind of got to filter
out with just some that bubble to the top like great and strain.
But I think there's something to that.
And that's specifically what the personality prediction algorithm, that's one of the key
tools that it leverages.
I think the psychologists do believe there's a systemic difference between the word choice
that people use.
And if they use different words for different strains, then that would be quite defined.
But here, I'll go ahead and get to the strains since I think that's the more interesting
part here.
But the idea is the application for this would be if you were going to say you were running
a dispensary, you had a bunch of users, well, you could basically try to ask for their reviews
and the idea is you could see if somebody left a review.
So here somebody just leaves a random review.
Well, instead of just saying, oh, is this a good or a bad review, you can actually measure
that and you can say that this actually wasn't actually the best review in the world.
So here this is a long one, but as far as intensity goes, it's sort of in the 23rd percentile.
And if you just start to pick at this, it didn't affect my mood at all.
I explored sativas and I still do, but I need a good indica.
Whatever that means.
So we may need to find it different.
But the long story short is, let's see if we, so this looks like a positive one.
You can basically determine, and once again, take this as all a grain of salt.
It's not the best data, it may not be the best data in the world, but the idea is if
you've got a corpus of reviews, you can then start to rank them.
So these actually may be user specific, but you can basically find out is this user above
average happy or less than average happy.
And then the real quick application is basically the idea is, probably a lot of people are
going to be happy with this, but the idea is if you're running a company, you want to
identify people who are unhappy and see if you can't rectify that.
So here you've got a bunch of reviews.
Well, here you can basically pick out the most negative review, if that was something
you wished.
Let's see if we can't print this out somehow.
Okay, so here's the most negative review.
And just from reading through this, this is almost something that if you're a dispensary,
if you were able to catch this and get back in touch with this person, you may be able
to salvage their customer relationship.
Because it sounds like they maybe had a mediocre time, got me slightly high, lasted less than
an hour, great pain relief, but gives me bad munchies.
So if for whatever reason you're able to, I don't know, provide a strain that provides
less munchies, which more research would need to be done there, then perhaps you could help
this consumer out.
But I'll quit worrying with that because I think this is actually the more interesting
part since this is where most of your questions have been so far.
So that's cool.
We've done that.
You can group reviews by user.
Well now we can actually group reviews by strain.
And we actually have an enormous amount of reviews for some of these strains, I realize.
So some we may not have as many.
So just for this analysis, I don't know if this is pertinent to do or not, so I'll let
you be the judge, but I restricted the analysis to those that have more than 30 reviews, and
then I selected 20 random reviews of those.
So the idea is that way we're kind of comparing comparable bodies of work.
That way the corpuses are about the same length.
I don't know if that matters or not, but I just kind of was thinking that you may get
a different reading from 600 reviews than you would from 20.
So please explore to see if this is pertinent to do or not.
But the idea is, okay, just like before, we can basically create essays for all the different
strains.
So these are basically 20 random reviews for these strains.
And so then, well, just like we said before, you can actually start to see which strains
people are talking more about.
And so this just was sort of a fun idea, but one of the effects was talkative.
So it would just be kind of funny to see if the effect talkative had any correlation to
the number of words in a strain's review.
And then now we can get to the fun part is you can actually look at all the words that
people are using for different strains.
And so, John, this is actually right up your alley where you can start to see are people
using gas or fuel a lot for particular varieties.
And today we're just sort of looking at positive, negative.
And so what you can do is you can actually rank the reviews for these strains from positive
to negative.
And so what this got me thinking about is you have these cannabis cups where everybody
brings in their variety.
I haven't ever been to one, so I'm just kind of speaking from what I'm imagining that they
would be like.
But I imagine people bring in different varieties and a lot of people try them and they vote
on the best one.
Well the idea here is now you don't even necessarily have to have people vote because that may
even be sort of an imperfect measure.
If you just ask somebody to rate a strain one to five, they may just give everything
a five.
So that may not even be the best measure.
But what you could do instead is you could have everybody write a review about the strain
and then you could see how positive they are and then you could see who the winner is.
Drum roll please.
And so with the random sampling, we have the strain with the most positive reviews being
conspiracy kush.
I just wanted to show you if you don't do the, so you may actually just want to actually
just see which strains are maybe just in the top percentile.
So let's see if we can't do that real quick.
Just say which strain is greater than the quantile 90th percentile.
I think this will work.
Cool.
So now you can basically get your top 10 favorite strains out there.
As I said, this is the first time I put this list together, but I think this is valuable.
So if you're going to go grow cannabis, maybe you want to start with super blue, super skunk
or blueberry cheesecake, chocolate, lemon.
What if it's supposed to be lemon?
But you may want to see if you can't start with the strain that people already kind of
like.
Cool.
So that'll sort of tie us in nicely to the last part here and then we'll get back to
the cannabis industry.
So once again, just coming at this from a statistical lens with full knowledge that
this data set one is imperfect, two, it doesn't take into consideration the location of the
users and all of this.
Many, many potentials for downsides, but the idea is if you've got your own body of reviews,
you can hopefully use these exact same statistics.
Cool.
So first things first, just going to rate every single review from positive to negative.
This is something that, well, I guess we haven't really proven that it works yet, but we may
never really prove that it works, but the idea is hopefully this can be a tool for you
to use.
So if there's any way that you can use these tools to make better business decisions or
decisions as a consumer, then more power to you in my book.
But the idea is, okay, we can actually see if there are any reviews here for the Jungle
Boys.
And pardon me that I didn't realize that it was with an S. For some reason I thought it
was with a Z, but I've got multiple spellings here.
Just pardon the code.
But the idea is we've got tons of reviews, so it would be nice if, so this isn't probably
a large enough subsample to draw any meaningful conclusion.
We only have seven samples.
But we do have seven reviews where people specifically mentioned the Jungle Boys.
So some gelato, orange cookies, and wedding cake.
And once again, don't read too much into this because I think you would have to do a difference
of means tests to see if, what I'm going to do here is we've got our rankings of all the
reviews.
These are all the reviews positive to negative.
And the average is around 0.67.
So the nominal value, like where you actually are, in my opinion doesn't really matter too,
too much, right?
Because most people are having a positive experience.
The idea is are you above average or below average?
So basically what you can do is you can basically calculate the average intensity of the Jungle
Boys reviews.
So here are their products.
You see this one really high.
This one really low, right?
0.51 is below average.
But the idea is, oh, and look at this one, 0.32.
That one's well below average.
But the idea is if you were going to take the average of all the reviews, and so if
you, as N goes large, you want your sample size to get large.
And I'll back it out because conditions matter.
And what do I mean by conditions matter?
Well, what if there's a difference, a systemic difference by state?
So what if the people that gave the low reviews, what if they're in a particular state?
And the people who gave the high reviews are in a different state?
Well, that would have potentially meaningful implications.
So as Kimberly pointed out, that matters.
We don't have that data.
And we're just taking an unconditional average, which as I pointed out, hopefully in many
meetups before, the more conditions you can add on your averages, typically the more interesting
your statistics are.
I think you could write paper upon paper just by taking conditional average after conditional
average and then doing a difference in means.
There's no need to go do machine learning models when you can just do a difference in
means test, in my opinion.
But I don't know why I come at it with that perspective.
But I do.
But long story short is, if you were going to take the average sentiment of the Jungle
Boys reviews, at first glance, it does look like they may be above average.
As I was saying, we only have a sample size of seven.
So we probably couldn't actually draw any statistical significance from that.
But the idea is you'd want to get as many reviews as you can, get as good quality reviews
as you can, and repeat the analysis yourself.
So that's what I had to say about the statistics.
Next week, we'll finally get to personality, maybe look at some other applications of natural
language processing.
Because as I said, there are some other ones we can do here.
The aspect of extraction is where we basically find effects.
We can find the headache, whether it cured a headache or caused a headache.
The personality prediction, I think, is going to be cool.
Depression, I think, is going to be really cool to study, because that's something that
people even talk about as one of the reasons why they may use cannabis.
They may be seeking to alleviate depression.
So what would be incredibly helpful, interesting, would be to if you had a body of reviews by
a user that are time stamped, then it would be interesting to see if their depression
is changing over time.
So maybe their reviews may indicate that they're really, really depressed.
One, maybe their depression may wane, or maybe their depression may increase.
The idea is if there's any way you had access to user reviews that are time stamped, say
you're a dispensary, then one, you could monitor to see if any user is becoming unusually
depressed, which that may be a, you wouldn't want that.
And then two, if somebody is actually getting depression, they're alleviated, you may want
to see what strains they're using, how much are they consuming, how are they using cannabis
to help them out in their life.
So I think that's an incredibly, incredibly cool one to study.
Engagement measures.
What reviews are you using here?
Is this what we call the 42,000 set?
Yes.
So the one we've been working with for a number of weeks.
Exactly.
I think we should now call it like 22,000 set.
But that's what I was saying is like, almost like every time we go out panning for gold,
it's like there's just always like, and I'm saying like, this isn't necessarily gold because
this isn't statistically significant, but I'm just saying it's a demonstration of how
you can use the model to find gold.
So I think that's gold in and of itself.
But the point being is it's almost like every time you go and pan through this cannabis
data, it's almost like you can't help but discover these interesting ways to look at
the data.
So can I make a, have you looked at the username count?
In other words, most of the, you have, if I remember correctly, about 350 anonymous
that you throw out.
And then as you move down, the next most frequent is around 70 odd.
And then there's a whole series of 60s and 50 reviews.
And then of course it goes all the way down to ones.
And my sense is what one could make a good living with is look at, take maybe the top
20 most frequent reviewers and look at that against the top 10 most frequent strains or
something and see what kind of a living you can make.
It might be better to subset it and work accordingly.
And you're definitely right, John.
And the way I would describe this is there may be some knowledge hidden in these consumers
that respond a lot.
So if they just leave one review, it's hard to really know what to make of that.
I mean, there's definitely potential insights there.
So I don't think you should just throw it away.
As for the analysis today, I looked at the people who left more than eight reviews, which
I think was around 90 people.
And once again, that's just the data that we had here.
And also I think I may be running...
So for example, if you take the reviewer who has left 70 reviews and their handle is Chill
Panda and you look at their energetic uplifted response versus their relaxed sleepy response,
you find that they flip into the inverted response class.
It's real clear.
Others don't.
And so my intent was to go through some of the top reviewers and see how successful I
can be at assigning them to response class.
But if you're going to be dealing with this globally, I think you got to stratify your
users, your reviewers into the appropriate response class before you draw too many conclusions.
I hope that's clear.
I can show you guys in a couple of weeks as I progress through that.
I'll have some figures for that.
You're definitely clear, especially in that we need to now control for different users.
And this is why I really wanted to move into personality was because those are five metrics
we can add on a user by user basis.
If you have any other ways to add user specific data, then you can supplement there.
I'll create this data set or this response for the top reviewers and see how well I can
assign them to response class.
And then if you want to see how that matches against any personality testing algorithms,
go forth and prosper.
I think that would be probably a good challenge.
Whenever you're doing these kinds of analyses, you want to tell a story that hangs on something
else.
Otherwise, it just hangs out in the breeze and it doesn't go anywhere.
You got to tie it back to a story.
Exactly.
And speaking of which, hopefully I can get through this.
So I think my computer may be about to run out of juice here, but hopefully I can at
least tell you the final bit of the story here to bring this all home.
So where's this all going with the sentiment analysis?
Well, so basically there was the frustration at the beginning that I've talked in with
essentially legacy growers in Florida.
And I made the mistake of saying, oh, things are going pretty good in Florida, huh?
And they said, yeah, if you're a leaf or truly, it's going pretty well.
And so what do they mean by that?
Well, if you see who's licensed to operate in Florida, you'll see there's less than
two dozen or so companies.
So I try to be a glass half full person and say, hey, at least some's better than none.
But then my economist gets in me and then I start seeing like, oh, like in Oklahoma,
you've got hundreds of different retailers throughout the state.
And so what did they mean by what they said?
Oh, it's pretty good if you're a leaf or truly.
Well, it's like, oh, like this is kind of what they meant.
It's like, you know, all of these could potentially be independent companies, right?
And that's exactly what you see in Oklahoma, right?
Instead of 24 companies with hundreds of locations, you actually see hundreds of businesses, right?
And I think that's so, right?
So I think we now need to look at this data and see, okay, what's actually going on here
in Florida.
And I think that's sort of what some of the criticism is, is they say like, what's going
on because I was actually speaking with an investigator at the Bureau of Cannabis Control
in California.
And I said, you know, everyone's talking about diversion.
You know, what's it like?
You know, is it like a box of eight pens that fell off the back of a truck?
And he said, no, I went to a location that was supposed to have a million of eight pens
and there were none there.
And he said he can and did pass that information up the line, but that's all that he really
could do there.
So basically what I think people are frustrated about is there are supposedly agencies regulating
this.
However, everybody knows that the cannabis is being diverted and sold illegally.
Yet we're somehow supposed to pretend, you know, that's not happening.
And what we observe is right in Florida, there's many poor or working class farmers that would
love a chance to grow cannabis.
And they see their government agency, you know, award the contract to a company from
the West Coast, where in my opinion, I think we need to change the null hypothesis here,
right?
Instead of the null hypothesis being there's no diversion, can you prove diversion?
As far as California goes, in my opinion, I think the null hypothesis should be there
is no legal cannabis in California.
Please provide evidence if there is.
This is where I am not trying to point fingers because I think everybody's just following
their incentives.
I don't fault anyone on the West Coast.
In fact, people on the East Coast may not have cannabis if it was not for their efforts.
They're following the incentives.
They're doing what the incentives lie.
So I don't think anybody should be frustrated with Jungle Boys.
And in fact, it looks like people generally really like Jungle Boys and they really like
their products.
So if there's anyone to be frustrated at, I think it would be the Florida Department
of Agriculture and every single politician in Florida.
And I would now flip the null hypothesis here, where the null hypothesis would be there's
no fraud, there's no corruption, there's no money being changed under the table.
And I'm not making any accusations here, but I would flip the null hypothesis.
And I would say, I want to see evidence that things are legitimate because ask me what
my null hypothesis is.
And so that's sort of, I think, what people's frustration is in Florida.
I think they don't know who to be frustrated with.
They want to be able to participate.
They just see everything kind of get handed over and it's like they don't want to be
frustrated at Jungle Boys because they may identify with Jungle Boys.
And as I said, I don't necessarily think they're doing anything wrong.
That's just where the incentives are.
So basically, if you're in Florida and you're frustrated that you can't join in on the fine,
I think you kind of got to look at your politicians.
And that's sort of where I kind of just have kind of just kept following this.
And I kind of identify with the frustration.
And I'm not trying to step on anyone's toes.
I'm out here on the West Coast.
I am sure I can assume some West Coast cannabis in my day.
So no hard feelings to anyone.
Really I'm just saying like, hey, I think if there's any frustration, I think we may
need to have some frustration with our politicians.
And in my opinion, vote for someone younger, right?
We need a solution.
So we looked at the data and I'm not trying to be ageist or anything, but we did look
at the data and the younger crowd is generally more on board with cannabis being permitted.
So that's all I would necessarily do.
Like if there were two candidates running against each other, I don't even know if I
would look at political party.
I would just go with who's the younger chap or gal.
So I don't know.
Those are my thoughts.
I'm not really trying to have this be a political at all.
I just kind of share with some of the frustration.
But at the same time, maybe this is something to be celebrated.
I don't know.
I'll kind of pass this on to you, right?
Because should we be a glass half full?
Like is it awesome that Jungle Boys are coming to Florida?
Like are they knocking down the wall or will the Florida growers get their chance at this?
So that's my spiel.
Those are my thoughts.
I'll pass it back to you.
So today was already a controversial day, so may as well make it 100% controversial.
But any thoughts, comments, questions before we call it a day?
Thank you.
That's really good to know.
So I don't even know what the Jungle Boys are.
So I'm going to Google that.
And I just left Florida.
I'm four hours from Massachusetts.
And as soon as I get back, I'll get settled and then I'll start getting re-engaged.
Thank you so much, Keegan.
Thanks, Candace.
And you're the person who's in Florida.
So don't let me tell you what you should and shouldn't do in your state.
This is up to you.
I agree with you.
I think what frustrates me with Florida is I can grow weed up in Massachusetts.
No problem, right?
Because I kicked all those pharmaceuticals, right?
The Parkinson disease pills, everything.
You saw the list, right?
And so it frustrates me that I just can't grow in Florida.
I have to actually buy medical marijuana.
But I'm grateful for that, that it's available.
But no, you rock.
I think it's great.
Thank you so much for what you do.