Welcome to Cannabis Data Science.
Thanks for holding tight.
Promise it's going to be worth your while today.
So have yet another new idea to share with you today.
And also want to share a lot of ideas about the group and kind of get everyone on the
same page.
Kind of talk about what we're doing here.
Like what's the point of cannabis data at the end of the day?
So I'll get into that momentarily.
But before you formally kick off, for those of you who are new, my name is Keegan.
Started Cannlytics.
So anyone can help out with statistics in the cannabis space.
I think the cannabis space is a place for everyone.
So grab your skills, grab any know-how you know, and come and join in on the fun.
So the idea is it's not an exclusive club.
Some industries are really hard to break into.
And I think it would be real cool to see the cannabis industry be a place that's open to
everybody.
And so Cannlytics is leading by doing.
And so Cannlytics is open to everyone.
Any skill set.
And so the idea is, I mean, myself, by and far, far from the greatest of whatever you
may want to call one's self, whether it's a statistician, economist, data scientists.
I may use those words, but people may disagree, you know, if I am any of those things.
But the idea is I think that we can put all of our minds together with these awesome tools
that we have here, right?
The internet, computers.
And I think we can make a positive impact to help a lot of people.
So the idea is, you know, why should we just, you know, stand by and let all these golden
opportunities get snatched up?
So this is a place that I think should be open.
And you know, anybody is welcome to come here, share your ideas, share your research.
And the idea is by bouncing off our ideas together in an open manner where anyone can
participate, values open to everybody, that we here can generate more value than you can
with a treasure chest of six hundred million dollars.
And I think we've already demonstrated that.
And I would love to continue demonstrating that.
And so I'm going to try to drive that point home here today.
But that's what the group's about.
And so without stealing the show, we'd like to spend today as a pretty open discussion
so everyone has a chance to speak.
So you don't have to.
You're welcome to be a fly on the wall and listen.
But if you want to speak up, speak up as much as you wish.
So in no particular order, just starting in my top corner, Jacob, welcome to the group.
Would you be interested in introducing yourself and what you hope to get out of the group
or do in the canvas space?
And no, no pressure, as I said.
Or if you're taking a second to toggle your dials, I know it took me a second to then
just chime in when you get those set.
How about you, Candace?
Anything on your mind?
Anything that you're hoping to achieve this week?
Thank you again.
Let's see.
I'm still working on the ERD, the markdown, and I did get the Panda profile reports for
all the Washington State 2021 data set, and I'm just going to stuff it into a little PPT
and we can start, you know, just kind of keep chunking away on that Washington State data
set and who knows, you know, my call pretty cool.
And then there's some other things.
And then once we do some data analysis on it or get the data better, you know, we can
start coming up with a bit of a plan and then I can, you know, put it up for everybody.
And this is phenomenal.
So a big complaint is that people trying to learn data science, they don't have good data
sets to work on or they're all working on the same data sets, right?
You all worked on the same Twitter posts and it's like, you know, how much can we scrape
these Twitter posts?
And I think that's one of the fun things about the Cannabis Data Science Group is where we
have novel data sets.
And we have to scrape them out of PDFs or APIs or what have you.
But they're fun.
It's real data.
So we get real challenges.
The Washington State data is one of the best examples of messy, messy data that a good
data scientist needs, is needed to clean up and analyze.
So your work on that is going to go light years because once you get that data set approachable,
then people all across the world can use this, even if it's just as a test, right?
So instead of testing your machine learning models on Twitter posts, you can test your
machine learning models on Washington State traceability data, which is kind of cool in
my opinion.
So fantastic work, Candice.
This will pay off here in the coming weeks.
So thank you.
Oh, and yeah, Ryan had put in a note and he said Canalytics.
This one is C-A-N-N-L-Y-T-I-C-S, Ryan.
But you can take it.
Sorry, Keegan.
Ah, so Canalytics.
Yeah, Canalytics.ai.ai.
So thank you.
Oh, you're welcome.
Thank you so much, Keegan, for what you're doing.
And everybody, everybody that tips into Jerry, Joe, and everybody.
It's everyone, I mean, awesome.
Thank you.
Candice, can I ask you a question about the Washington data set you're working with?
Sure, John, if that's okay, Candice.
Oh, yeah, sorry.
Playing with, I guess, toggling the switches.
Yeah.
No, quick question is that our own organization has an interest in ingested products at this
point and kind of typing the classes.
We've done an initial pass with just survey data that we've collected from users in California
that are patients of my clinical colleague, Jean Taleran.
Does the Washington data set, does it have fields that speak to ingested data and ingested
products and potentially we could look at that to try and figure out the type and frequency?
Well, they're not doing like per customer dosing per se.
No, no, but do they talk about gummies versus chocolates versus mints versus beverages?
Yes, and Keegan has used that data set to in past YouTube videos showing that too, I
think, the way with Washington State, right, we've done Washington State, Oklahoma, all
kinds of data sets.
But yes, the answer is yes.
There's all different types of flour.
There's edibles.
There's, I don't think they actually break it down.
Keegan, correct me if I'm wrong.
I don't think they break it down into like whether they're chocolates, gummies, but they
do kind of, yeah, they do indicate that.
Well, many thoughts because you two both raised many good ideas here and points.
So where to begin?
I would begin by saying once you find one gold nugget in a data set, then there could
easily be more.
So as you raised out, I raised the point, there's probably more gold in the Washington
State data.
Sure enough, John, I think we can approach your question because we can look at solid
and liquid edibles.
If we wanted to get fancy, we could use natural language processing and try to figure out
which ones are chocolates, which ones are gummies, so on and so forth.
But that would be a cherry on top.
And then as you said, so basically, John's after the types.
So this would be THC to CBD ratio.
And sure enough, we've got that data.
So with the 2022 data, we still have to make that link.
So we're still missing some IDs that we still need to request.
But up to the end of 2021, we can match lab results to edibles that were sold.
So we can see the distribution of THC to CBD in edibles that were sold in Washington State
of the full population.
Excellent.
So easier said than done, wrangling this data.
So Candice, now you actually have a demanded statistic.
So we now want to know the THC to CBD ratio, which is a statistic for every single edible
that was sold.
And then we can subsequently create more statistics.
So you can get fancy from there, but maybe there's a pattern in rural versus urban or
who knows.
So that's where you can start getting fun thinking of research questions.
But first things first, we'll get the first statistic.
So Candice, you want to start on that?
You're welcome to.
What do you mean on the first statistic that we have?
Are you on the THC ratio for all Washington State data 2021 edibles?
Is that what you said?
Essentially, yeah, we'll have to jump through a couple hoops.
We'll have to get all the edibles.
Yeah.
Oh yeah, yeah, we'll get it done.
And then essentially just take the maybe total THC divided by total CBD.
Sounds good.
If I might just make a point here, if the data contains non-THC, non-CBD cannabinoid
content fields, that would be exactly the goal that we're trying to approach.
So maybe when this moves to a point where you're willing to present this, maybe that
would be a good opportunity to see what can be mined from it.
So what you're talking about are like other ones like CBN, CBG.
Yeah, CBG, CBN, THCV.
If that in fact are fields in the Washington State data.
Right, or like the fields that are in skunk effects.
I'm sorry, say that last one again, Candice.
Like the fields in skunk effects.
Can you hear me okay?
Yeah, yeah, yeah.
Am I too loud?
No, no, you're perfect.
Okay, I did test.
Just not to rain on your parade, but I think you may have the best luck with the THC and
CBD because I mean it would be fun to see if there are any CBG, etc. edibles, but just
from my experience seeing a bunch of edible lab results, I just don't...
I mean the predominant ones you see of course are CBD, CBDA, THC, THCA.
Wait a minute, repeat that real quick.
So okay, CBDA, CBN, CBG.
So basically you should expect to see CBD, CBDA.
CBDA, okay, got that.
THC, and then THCA.
THCA, okay.
So you'd expect to see...
And then THCB.
You would expect to see mostly THC and CBD.
It would be curious to see if there's any acidic left.
So CBDA and THCA, I would expect to be zero because the idea is when you process it into
an edible, you typically have it go through decarboxylation.
So you turn the acidic to their...
John, you probably know the correct word for this.
Carboxylated or neutral form.
So they're psychoactive at that point.
So you would expect...
But I mean they still may have trace amounts of CBDA and THCA.
But long story short is these are typically ingredients that would be added as isolates
or distillates into a soda or into an edible.
So it would be unlikely for somebody to be adding in CBG, THCV, CBN, et cetera, but they
may.
So for example...
And Keegan, they are.
They're rarer products and that's why it becomes of interest because we're trying to tease
out the story on those.
No, they definitely exist.
We have them and others have them in their product databases that we've been looking
at.
But as you point out, of course, it's just rarer because the market hasn't moved in that
direction yet so much.
But it is a very fruitful, interesting area and there are big players looking at this.
So let's just keep an eye on...
They're called rare cannabinoids for a reason.
And you know what else is rare?
Gold.
And so that's basically what we're doing.
We're panning for gold here.
So we've got the tools.
So basically we can look through this data set and see if are there any edibles that
were sold with CBN.
So I think I've even seen some at the store and basically I guess it's not really a claim,
but the link that's trying to be advertised is maybe CBN maybe correlated with a sleepy
effect.
So I don't know how people are marketing the compounds, but this is something that we can
investigate.
So we've got some cool tools in our belt.
So what I would do is look at natural language processing plus these rare compounds.
So try to find any edibles that have CBN, etc. in them.
Use natural language processing on the sample name and try to figure out are they capsules?
Are they chocolates?
So on and so forth.
So that's where you can get fun with it to see if there's any insights that you could
pull out.
And then finally I think the business question is what's selling?
So if you can find some CBN edibles that are selling off the shelf in Seattle, then that
would be a valuable find.
And the data is just sitting there and I don't know if anybody's looked at that, at least
not publicly.
And so now we've got a bunch of awesome data scientists here.
The question's been posed.
We've got all the open source tools in our tool belt.
So I think we can answer this question for you.
So a coming question or topic I think for next week is so how do we get compensated
for our work?
And a nifty tool that I'll be sharing with you is essentially...
Okay I'll go ahead and let the cat out of the bag.
So basically what we're looking at here is basically the word has gotten a little overused,
a little hyped.
But the idea is we can basically create non-fungible tokens, NFTs, for data as well as algorithms.
So the idea is we could all write little bitty algorithms that help collect data.
You would have ownership of the algorithms you wrote and you could potentially buy or
sell them.
And then people could then pay you to use your algorithms.
And so the idea is Candice may write a nifty algorithm.
I may use another algorithm.
Maybe that incorporates Candice's and maybe I pay her a fee to use hers.
Or maybe I write my own.
And then maybe John wants to come along and then use those two algorithms to get some
data.
And so the idea is John could pay a nominal fee to Candice to use her algorithm or to
me.
Hopefully the fee's low enough that it's well worth John's while.
So pennies on the dollar is the idea.
So then Candice gets compensated for writing the algorithm.
John gets the data he needs.
It's all perfectly traceable.
So there's no question about where did this data come from, how is the data cleaned.
It's all essentially on the blockchain.
We can decide which blockchain.
So I'll talk specifics about this more next week because this is something I've...
I'm standing on the shoulders of giants.
So this is a project that somebody's already done here.
So long story short, this is a project that somebody's already done.
We're just wrapping it up into Canlittix.
So if you're particularly interested in this, let me know because I'll share more about
the project details with you.
I'm trying to keep it a little hush hush.
But at the same time, I think because we're this open group, I don't think closed groups
can compete with us.
So that's why I'm fairly open about our ideas.
So for example, this SkunkFX model that we just built, we built that in a matter of two
weeks or so.
And yes, it's got its flaws.
Yes, it can be improved.
But this is a model that would have taken a corporation.
What?
I mean, they would have waited to file the trademark.
They would have waited to file a patent.
This would have been six months to two years out the door easily and millions of dollars
spent on what?
And so here we just effortlessly made a prediction model in a matter of weeks that companies
just can't touch.
So that's why it's been sort of slow demonstrating the power of Canlittix.
And I mean, we have all the greatest data scientists in the world on this.
And the idea is now we just need to get them compensated.
And that's exactly what the data NFTs, the algorithm NFTs bring to the picture.
It finally adds a mechanism for all of you great contributors to finally get paid.
So now you can get paid for your algorithms.
You can get paid for finding data.
You can get paid for sharing data.
And I think this has been a critical missing piece of the puzzle.
So this will be getting built in the coming weeks.
So it's sort of a call to action right now.
So if any of you want to help contribute, then at the moment can add you on to authorship.
You can share in the copyright.
So at the moment, we can share copyrights and get this built.
And then at that point, you can actually get paid for writing algorithms or cleaning data.
And I think this is going to be revolutionary.
So as I said, this is a project that somebody is just doing in general.
It's just general data NFTs, general algorithm NFTs.
And I think we can get it to catch on here in the cannabis space.
And I foresee things like this catching on in most fields.
And then that way, data scientists like yourself could actually get compensated.
So I think it's a cool project.
We'll just be an example of it.
Keegan, can I just follow on on that?
Because as this gets discussed within this group, I would just point out that there's
an organization called the Ethical Data Alliance, which is absolutely doing this, has some grants
from some IT organizations to help move this along.
It's in the cannabis space because that's their focus.
And they've been talking about or they are creating something called the Ethical Eden,
whatever that is, the network that this would be on.
So I would just say it's probably worth looking at or talking to folks in EDA just to see
how it overlaps or complements or whatever.
This is going on.
And so it's worth being aware of it.
Exactly.
And I kind of want to tie this into something where I often say here, oh, you know, we're
advancing cannabis science and we're advancing statistics and this and that.
Does that mean that on the greatest mine out there, advancing the greatest theories, doing
the greatest work?
No, no, no, no, no, no, far from it.
That was sort of the point at the very beginning is we may be some of the least skilled.
And we're just doing the best we can.
But the idea is if we get a bunch of small fish together, we can form a giant school
of fish.
And the whole idea is stand on the shoulders of giants.
So the idea is these are awesome tools that some of the brightest minds in the world are
making.
We don't necessarily need to make them.
If we use these tools, that helps advance the field.
So in my opinion, so if we use statistics, I think that helps advance statistics.
If we apply statistical models to the cannabis industry in a new way that nobody else has
done before, I think that helps advance the fields.
Are our statistics perfect?
No.
Does the analysis need to be done again?
Yes.
Does the analysis need to be done more rigorously?
Yes.
Do we need to do more homework?
Yes.
Are we asking novel questions?
I hope so.
Are we applying methods in novel ways?
I hope so.
So I think that's sort of the idea behind the contribution we make is somebody needs
to come back and do it much, much better.
But it's sort of like the law of large numbers, right?
You need to do a scientific test, right, thousands and thousands of times.
But the first one provides a lot of value.
And it could be wildly, wildly off.
But it gets people thinking.
And then somebody can come along and test it again.
And then test it again, test it again, test it again.
And so what is the actual result?
Easily, could change over time.
So as I said, take all of our analysis as a grain of salt.
But I think the value we add is we may introduce an idea into the space, at which point better
and greater minds can iterate upon this.
So that's what I mean when I say I think we're advancing cannabis science is I think we're
just treading on ground that no one's gone before.
And I think there's value to that.
But we do need to do so in an ethical manner, right?
When you're treading on new ground, you can't just be leaving a bunch of garbage all over
the place or making a mess for people who may wish to follow in your footsteps.
So we do need to tread carefully.
So that's getting a little abstract.
So if you're interested, I've got a quick 15 minute presentation to share with you.
And then John, do you have any work to share or?
I have a couple points I wouldn't mind bringing up to the group at this point.
So sure, but if there's time, would you mind?
Well, would you mind if I ran through just a please?
Well, actually, actually, if you're OK, I'll just run through just a teaser of the work
I'll be working on.
And if any of you want to lend a hand and then, John, I'll give it the floor to you
for the last 15 minutes or so for you to.
I don't even need that much.
OK, well, without further ado, I'll just share with you real, real quick what I had been
thinking about.
So this you can think of this what it was, but this is sort of the point about canlytics
is what do I value?
So this is just a fictitious quote that I heard one time that I really liked a lot.
And so to me, I personally think code is maybe one of the most valuable things out there.
So that's what I value.
And that's what canlytics values.
So if any of you want to share in the copyright of the code, I think that's a valuable thing.
And so I'm more than happy to share that with you.
So please, please email me if you have any contribution and a contribution can mean anything.
So really, you know, if you help help share ideas or share data sets with me, I would
love to share authorship with you.
And I haven't been very good about that in the past.
So please be in touch with me and I'll be happy to share authorship with people.
Abundance.
Next, going to be rigorous here about following the scientific process.
So it's just going to outline real quick the steps of a quick research question.
So basically, the research question that that I posed, but I kind of want to formally look
through it in a scientific manner is I've got this hypothesis that a cannabis consumer's
personality affects the type of cannabis that they choose to purchase.
So just to kind of just, you know, stab in the dark, I was thinking that, oh, you know,
maybe just, you know, a hypothesis is maybe a little more than a guess, right?
It's a guess or, you know, my belief from just what I've observed in the world.
And basically, that right, there's introverts and extroverts.
It's a bit of a scale.
It's not a zero or one thing.
So we're expecting a scale and there's other personality traits, but that's maybe one of
them.
And maybe they like cannabis differently.
So I've got this hunch that maybe extroverts may like more of the indica type strains.
And then maybe the introverts may like more of the sativa like strains.
Is there anything to that?
Who knows?
We can kind of test it statistically.
So how do we go about doing that?
Well, first, we need to look at the literature to see who's talked about this before and
what concepts in the scientific literature can we use.
And the big one I wanted to use is one, revealed preference, in that an economist would say
you can't just ask somebody about themselves, about their preferences.
You have to determine that from looking at their actions.
So the idea here is we can't just ask somebody what their personality is.
We have to determine what their personality is from their actions.
Same with purchasing patterns.
We can't just ask people what they want to purchase.
We actually have to observe what they purchase.
Next, what are we even talking about when we're talking about personality?
Well, this is cool because now is where we kind of get the sample from different fields.
So we get to sample from psychology and say, cool, the psychologists have studied personality
and they've widely agreed upon five personality traits that kind of are on a scale.
So I'm treating it as zero to one.
So five traits on a scale, widely criticized and also widely referenced by psychologists.
Cool.
So we've got some shoulders of giants to stand upon.
Next, we need our actual methodology.
So what's our actual statistical framework that we'll be using?
So I picked the simplest statistical model in the book, the ordinary least squares regression.
I suppose difference of means or something like that may technically be a simpler method
of analysis, but this is pretty simple.
The idea here is how are we even measuring indica versus sativa?
Well, thanks to awesome work being done by John, it's appearing that the ratio between
beta-pinene and d-limonene may better characterize the sativa indica dichotomy.
So instead of using a dichotomy, we'll use a continuous variable R, which is going to
be the ratio between beta-pinene and d-limonene.
We'll take the average by user.
So maybe a user may pick a bunch of different strains, but maybe on average they'll pick
out hopefully strains that they'd like.
Or they may accidentally pick out a strain that's high in d-limonene and not like it.
Or they may accidentally pick out a strain that's high in beta-pinene, love it, not like
it, who knows?
The idea is after they try many, many, many samples, they'll on average hopefully be picking
out the ones that they like.
Will this hold up to the data?
We'll see.
And then the idea is we can now have the five personality traits, openness, conscientiousness,
extraversion, agreeableness, and neuroticism, which is basically sensitivity.
I think they called it emotional sensitivity or something like that.
Closed to new ideas is neuroticism.
Say that one more time.
Closed to new ideas.
The emotional one, the neuroticism?
I think the comparison is openness or closeness to new ideas.
And I think that's what falls into neurotic.
It's considered closure, a lack of openness.
So I've got to do more research myself.
I think closeness would be the converse to openness.
I think this is basically emotional stability.
So are you highly emotional or are you stoic?
Do you let your emotions show or not is the neuroticism one, is the way I understand that
one.
And then agreeableness, agreeable, disagreeable, extraversion, introversion.
I don't really know what the, I think the conscientious versus intuitive.
So is this, like I said, I don't know too much about this.
This one may be the logic versus feeling one.
But anywho, please read up on these because this is a deep, deep subject, right?
I mean, this is the field of psychology, so there's a lot of meat here.
But the idea is if we scale these zero to one, right?
Probably no one's...
I have a question about that.
If you go in and take all of those personality types and scale it zero to one, you should
have two words there and not just one term.
It should be openness, closeness, conscientiousness, whatever the opposite of that is.
You're 100% right.
And so I just grabbed these from Wikipedia, but please double check me on this because
the idea is these words are supposed to convey a scale.
It's basically like zero to one extraversion, zero to one agreeableness.
So would zero be introversion or extraversion and one, which would one be?
Good point.
I'm basically treating one as extroverted and zero as introverted.
Can I just make a comment here because this is precisely what we're facing as we talk
about these kinds of scales in our dosing project work.
We have a really great collaborator who's a clinical psychologist in the form of Jeffrey
Terrence out of Eugene, Oregon.
And so we do spend time talking about this.
And point of fact, in some of our most recent discussions, he says, no, don't go binary
on this.
It's better to do it as a bit of an ordinal scale.
It's got a term, I think it's called Langford or something, scale.
And he's convinced us that as we do survey work, we need to not be doing it binary, but
in fact asking folks for if they're responding in the moment or whatever, but it needs to
be kind of a scale, Jerry, kind of along the way you're saying.
I went into this.
I'm so impressed with the binary.
That just poses something close to the link that I looked at.
And I think that explains it very well.
Okay.
But you're 100% right.
I put it in the chat.
So there's different words for these.
And so that's actually one of the challenges you'll run into here in the psychology literature
is they're kind of always using different terminology for these.
But I just tried to standardize it the best I could.
But you're 100% right, Jerry.
And that...
Well, if you look at what Candice linked you put in the chat, it gives you the both sides
of the scale.
Spot on.
So we clearly raised...
So this is what's awesome about presenting this research to you is we get to find out
really quickly what points need more clarification.
And that's what's awesome about working with you all.
So this is not just...
This is a team effort.
In my book, this is team research because your feedback is, in my opinion, part of the
most critical step.
So here, I presented an idea to you and then you bounced back that, hey, we need to much
clearly define these personality traits.
The scale seems like a good idea, but we need to clearly define both ends of the scale.
Awesome.
So that's essentially all the points you raised have already improved the research.
So just by coming and sharing your thoughts, you, in my opinion, helped advance this research
question, which I feel is advancing cannabis science.
So that's what I mean by...
When I say, by you coming, I think you're helping advance cannabis science.
As I said, it may be a snowflake, but snowflakes add up.
So Keegan, if this really is of interest, there are established scales for this.
In particular, we're very familiar with one called Brunel Mood that we have used with
cannabis data.
It allows respondents to respond to a series of words that roll up into key moods.
We've looked, or we can continue to look, at how the acute cannabis response changes
your mood.
But one needs to select a scale.
Scale means a survey.
So for example, if folks want to go down this pathway, I would advocate looking very seriously
at established scales like Brunel Mood.
It's quite robust.
I love it, John.
And I love that you've already researched this topic.
I could present on this at a future meeting because, yeah, we have a good number of years
working together with Jeff Tarrant and Eugene on this.
So if this is relevant, let's, in a future meeting, I'll walk you through some of our
Brunel's data or Brunel's interpretations.
Well, we may have an awesome platform for you.
So hold tight because we may actually have a good time for you to present your...
John, could you put that scale in the chat so we get the correct spelling?
You know, Jerry, I don't do that kind of stuff very well.
Can I just suggest that you look up Brunel Mood?
I can spell it.
Spell it for me, yeah.
Yeah, B-R-U-N-E-L.
Yep.
You'll have no problem finding it.
Okay, Brunel Mood.
Brunel Mood or Brums, it's sometimes called Brunel Mood scale, B-R-U-M-S.
We'll find it relevant for it.
Thank you.
Very widely available.
And John, you've now helped our literature review because with my literature review failed
to unearth, you effortlessly provided.
So now here I'm shooting in the dark and all of a sudden you just now pointed us to, oh,
you know, this is the metric you need to use.
And so this is sort of my point is, you know, instead of me now going off on a wild goose
chase for months on end, which I see happen all the time, like at universities and whatnot,
right?
I could have gone on a six month goose chase.
And then I would have then presented my work to John and he said, oh, have you tried the
Brunel Mood test scale?
And then I would have been like, oh, well, no, back to the drawing board.
And so basically now instead of wasting all that time, I thought of this and then one
day later get to bounce it off of John, gets bounced back to my court.
So the idea is we can just do this rapid, rapid research that when you spend a bit of
time around, you know, university professors, one time a university professor specifically told me like, you know, things move slowly here.
You just have to get used to that.
So I'm sort of a go, go, go type of person.
And so whether research should be done that way or not, that's an opinion.
But I think in this open manner, we can do research quickly.
And at the Brunel Mood, and I just put the link in the chat of an assessment, and it
may be too, it may be so detailed as to make any result that we would come up with be insignificant.
I mean, it has 24 different mood characteristics everywhere from panicky to uncertain.
We want to add rigor to our analysis, Jerry.
So this could be a good way to do it.
So Jerry, we have we use it in the acute setting.
So we ask folks before they get high to fill this out.
You fill it out really fast because you're you're simple.
It's like a button click for does the word apply or how much the word applies at that moment.
I think what what Keegan's looking for is personality type in that mood.
Yeah, and that so brooms is an example of mood.
It would be if we're doing personality, we would probably find another scale or need
to.
I'm just pointing out that we have experience with the mood, the acute mood change using
Brunel.
OK, it's actually interesting.
The real point I was trying to make is not binary, but this Langford scale or whatever
is being important because I think I hope that's clear.
You know, the binary assessment is such a cool way of doing things.
But I think we're we lose, unfortunately, detail that way or the ability to to find
correlates or factor analysis or whatever.
If we're in binary mode is what Terrence was discussing with us this past week.
I like your mood analysis and that actually will be relevant to a quick a quick something
quick I want to show you today.
But the mood analysis could also be done to see if that's relevant.
But the other point I wanted to raise is the economist would come in and say, well, well,
well, well, well, well, you know, you can't just be asking people about, you know, their
personality traits here.
You know, what if somebody I wanted to mislead you for for some reason or another.
So or what if somebody just doesn't have time to take a personality test or this or that?
Well, this was a novel idea that other people have had, but we can now use it here in the
cannabis industry.
So I found a handful of examples of people doing this, as I said, on Twitter posts and
I saw eBay things.
So there's many uses for this, but no one's done it in the cannabis industry yet.
So I think we could maybe take a stab at it.
And OK, the idea is we've collected the consumer purchases and the lab results.
We also have the reviews for these purchases.
So now what we could do is we could use natural language processing.
Look at somebody's review and try to scale it on zero to one for each of the five personality
traits.
And why do I think that we can do this?
Well, there's been psychologists that have hypothesized that personality traits would
probably show up in language and specifically that perhaps, you know, really important personality
traits may often get boiled down to single words.
So it may not be the case.
It's not tested.
This is just a hypothesis.
Or it's not a theory yet.
It's just a hypothesis.
We need to rigorously test it.
But, I mean, what if it's the case that certain words are only used by really extroverted
people or maybe people who are really high in the degree of openness may use the word
open a lot or, you know, who knows what this is?
The idea is, you know, we could just read through a bunch of people's reviews and do
the best we could at it.
But we could, the idea is, use natural language processing and spend much, much more time
on it.
So instead of reading through each sentence, you know, really, really carefully, you know,
we can just let the computer read through these sentences at just lightning fast rate,
perform logical operations at lightning fast rates.
So all it's doing is what a human would do, which would be read the review.
Okay.
You know, we could train this on some data so we can actually say, okay, here's a data
set where someone's taken a psychology test.
Here are their actual traits.
And then we can, you know, see how well we can predict those with the language they use.
So that's the idea.
And the platform for this is, so I'll just show you an example.
And I was thinking about, I was going to float it by the group here before, you know, formally
booking it, but I was thinking, okay, Saturday morning is maybe not the best time for people.
So I was thinking about moving Saturday morning statistics to Thursday.
And I was just going to see if anyone's thoughts about this and if this time may work for you.
But maybe, you know, Thursday, I should have said from 420 to about 530 Eastern Standard
Time.
So the first 10 minutes or so will be pretty casual.
And then we'll do about an hour of, you know, rigorous analytics.
So that way, you know, the Wednesday group, we're just kind of having this light discussion.
And then, you know, next Thursday, we'll actually get into the nitty gritty of this and actually
go through all of the, right, because, you know, a criticism that's been levied is, okay,
we don't, we're not rigorous enough.
We don't do enough training and testing of our models.
And we may be providing misleading information because we're giving the illusion that we're
doing statistics, but the statistics aren't robust and people may be drawing incorrect
inferences from those statistics.
So the idea is we'll do a really, really robust session here.
So that way, you know, the Wednesday group, pretty casual and then hardcore on Thursday
where rigorous statistics, you get the results, we actually will have some faith in our results,
right?
So instead of just saying, oh, this is a mock or this is a proof of concept, no, like where
this is a rigorous analysis, we've tested, we've trained, we're confident, as confident
as we can be.
And you know, this is advanced cannabis analytics, right?
We're taking the most rigorous statistics out there, applying it rigorously to the cannabis
field in an open manner that anyone can participate in, anyone can contribute.
So that's the idea here.
My question about language is different people from different social groups use language
differently, especially non-English language, you know, first like either non-English speakers
or people whose English is not their first language.
So you raise this point, Jerry, and I don't know if it's a good or bad thing, but the
first thing that comes to my mind is, and this is from a statistical point of view,
a statistics professor once told me he looks for variability in his data, because if there's
variability, you'll be able to do good statistics with it, right?
Statistics is the science of uncertainty.
So the more uncertainty you have, the better statistics is a tool for that job.
If there's not much uncertainty, you can just go to math.
So I think we could actually use that essentially to our advantage in that, okay, that may very
well be the case, but introverts and extroverts may still use language differently.
So maybe English is somebody's second language, but maybe that may even make them even more
predisposed to using certain words.
So it could even be the case that we could predict personality traits even better if
English is somebody's second language.
May not be the case, but I think it does add more variability, but I think potentially
could use it to our advantage, but it's definitely a good consideration.
Can I make a couple of points?
And I think there's an elephant in this room.
I mean, it's an interesting idea.
What I might propose is, so you brought up earlier the chemotyping dichotomy or chemotyping
classification that I'm certainly advocating that folks adopt and ride.
So let's just say, for example, that's one of our principal discriminators.
So the chemotype of the botanical that's reported on, let's say that's point one.
So you want to see if you can use natural language processing or some sort of computer
algorithm to parse reviews.
Maybe the parameter there is how active or passively written the text is.
And what I mean by active is I used this bud to get high as active.
I would say this product made me sleepy as passive.
These are pretty important semantic distinctions.
And it may be quite easy to distinguish between an active review and a passive review.
So you score it that way.
That's all fine and dandy, but it hangs when you're done.
You've got to tie it to something for it to be relevant.
And that's the dilemma I have.
So what?
You come up with at the end two thirds of people like this chemotype who write active
reviews and one third like the other ones.
Then what?
I mean, where do you go from there?
That's the dilemma that I see.
It's just a pretty thing to hang out there.
It doesn't tie to anything.
And so I would encourage us to be thinking about, so what do you tie this kind of analysis
or this kind of report to?
The way you raised many good points.
So to address them in reverse order, what I would tie it to is predicting our hat.
So when you build this model, we often build coming from a learning statistics from a university,
people are often interested in estimating beta because they just want to know the relationship
between say openness and somebody's average ratio.
But in the business world, you're more interested in prediction.
So in the business world, you're more interested in predicting our hat.
So given O, C, E, A, and N, what would be our hat?
And so the idea is I think this could benefit a consumer.
So most people generally know their, well actually, well I don't off the top of my head,
so that may not be a correct statement.
But the idea is as a consumer, you could basically take a personality test and one, you'd get
your personality.
And then two, you could say, oh, on average, people with your personality prefer this type
of string.
So that way, before you've even maybe before you've even tried cannabis for the first time,
you now know what people with your personality, what type of strains they may gravitate towards.
It may be, there may be absolutely no difference.
Maybe everybody, maybe personality has no effect.
So remember all these betas, these could all be zero.
So that's the idea is we want to test to see.
I would say that people who market and produce and sell cannabis would want this information.
And that's the idea is, oh, these psychology traits, we're saying, oh, they get incorporated
into language.
Well marketers are all about their marketing type.
So it's like, oh, if you've got a strain that you think extroverts will love, maybe
you can somehow market that to extroverts.
Say, oh, you'll love this as a party or something like that.
I'm just, once I said, I'm just kind of stabbing there.
Psychologies I need to do a lot of reading up on.
But the idea is maybe like Jerry said, maybe marketers could use this in tailoring products
to certain personality types.
And then as I said, maybe consumers could use it to maybe make an educated guess about
what product they may like.
So that's the main idea in a nutshell.
As I said, a lot of the code is already written.
But your first point was how do we actually assign the traits?
And that's what I want to end up spending most of the time on Thursday talking about.
Because that's sort of where all the fun comes.
Because I said, I'm not the first one who's done this, but basically kind of like what
John's saying is, do people use passive or active sentences, so on and so forth?
And do those choices help predict personality?
And then there's a couple training datasets we can use.
So there's basically a training dataset where someone's written an essay and then they've
also taken a personality test.
So we can basically build a training, so we can train our model on the training dataset
and then we'll use it on our actual dataset.
So that's how we'll go about using natural language processing.
But that could almost use a whole meetup of its own.
So instead of Saturday, it'll be next Thursday.
And I think this one's going to be cool.
So today presented the methodology and then next Thursday we'll actually estimate it.
So now I think I'm just kind of just going on and I want to make sure everybody can get
on with their day.
So any last thoughts, comments, questions before we call it a day here?
For NLP processing, is it difficult to get personality traits of introverted persons
from language because they mostly don't review products or they don't participate in social
media and I guess it's difficult to get personality of introverted persons.
You raised an awesome point.
And basically what you raised is, is there a systemic difference between people who are
actually going about answering reviews?
And this easily could be the case.
So I talked about this where like the positivity bias, so maybe people who had a positive experience
are more likely to leave reviews.
And as you mentioned, maybe people with certain personality traits are more likely to leave
reviews.
I think that should be okay as long as our training data isn't biased.
So our training data should be about equal equal.
So actually I don't even know if that necessarily matters.
I don't know if you need an equidistribution of observations.
So I think it's an awesome point.
I'll need to think about this.
I want to think it's, I think we can work with this.
It'll just be our observed data.
There just may be more extroverts that we observe.
That should be okay.
But I think what you've raised is an excellent point.
And it kind of hits on sampling bias.
So this is a critical bias you've got to watch out for.
So thank you.
So thank you for bringing this point up.
I think if one goes down this pathway and so at the end the results that you get, you
get a predictive model or you get a frequency count or an incidence count or something.
So after you've done all your machinations, you come up with okay two-thirds fall in this
group, one-third fall in this group.
So what?
You have to, I believe, then pair this with something else.
And that might be actually some kind of interventional study or interventional trial where you then
have, it sets the hypothesis for the group and then you have to go out and actually do
it with people and see how well it matches.
Maybe that's the approach.
So what you're building initially is a hypothesis with the motivation to then go fund and do
some kind of interventional study.
Maybe that's its utility, but I'm concerned if you just do it in isolation, it hangs and
it's just another shiny pretty picture on the wall.
In John, you raised two critical points.
The first being is even this beta-pinene-d-lemonene ratio, the key metric we should look at or
maybe we want to look at product type, et cetera.
So our outcome variable we may want to think about, so maybe extroverts are more likely
to try, so does.
So there's many research questions we could ask there.
The second more important point you raised is, is this essentially just a hypothesis?
And my answer is absolutely.
It's essentially the value we're adding here is I think we're bringing forth or informalizing,
thankfully, it's not a novel hypothesis.
At least we're hopefully stating it slightly more formally.
But this is, like I said, a hypothesis that's been on my mind, so I just kind of wanted
to formally state it.
And then exactly, it's sort of that picture of the law of large numbers that we saw.
We're just t equals one, so we're just showing how it could be done with this ad-hoc dataset
that we happen to have in the public domain.
But if you then go and do a clinical study or do a study with some of your own data,
then that'll be t equals two, t equals three, so on and so forth.
And so the more and more you test this, the more likely the tests are to converge on the
actual effect.
So do I think we're going to actually measure the effect, the true effect, our first time
we study this, the first model we estimate?
Unlikely.
But hopefully the idea is we can get a lot of great minds thinking about this, and then
many, many people can estimate this model, test out this relationship, and then in the
long run, hopefully we can figure out if there's any rhyme or reason to personality in cannabis.
Is there anything to it?
I think it's interesting to think about.
So I'll let you all chew on this until next week.
Well, we've gone over, so I'll let you get out of here and enjoy your day.
Just want you to thank you.
Thank you all for coming.
Very interesting again, Keenan.
See you later.
Good discussion.
Thanks.
Thank you, Keegan.
Thank you, everyone.
Bye now.