Predictive Analytics

I read Tomorrow never knows, an article on whether or not sciences should be in the business of predicting…anything.

It was a good and thoughtful article and I agree with most of it, especially with “Predictions are not instructions that people simply follow to make better decisions. They are pieces of an intricate puzzle that may sometimes contribute to improved decisions.”

Penecontemporaneously (a word no longer in use in English, sadly, and meaning “all at the same time”), I’ve been reading Extraordinary Popular Delusions & the Madness of Crowds. Much of this book offers historical accounts of speculation markets and favored predictive methodologies throughout time, all of which seem to fail and all of which are based on individuals believing they could predict the future.

There are interesting points to be made by intersecting the two, me thinks.

The Function of Science

For example, the sole function of science, any science, is to explain the “world”. Science explains the world by discovering and defining laws, the Xs that equal Ys, that govern interactions.

This means science de facto is in the prediction business. If not, 2+2 can equal whatever you want and (follow the logic) then science is undeniably in the prediction business. If 2+2 can equal whatever I want then my ability to predict any outcome is predestined to be 100% accurate.

What neither article nor book makes clear is that it is people — business, politicians, educators, moms&dads worldwide — and not science that is responsible for any failed prediction because people seem to confuse scientific prediction with precognition (“foreknowledge”).

“Predictions are not instructions that people simply follow to make better decisions” is, in my experience, a very true and sad statement. People get upset when they follow instructions and the results don’t look like what’s on the cover or page or website. And “decisions” imply human conscious volition, not an abdicating of responsibility and will. I know people prefer to have no responsibilities, I don’t know many people who wake up each morning and proudly proclaim “Today I’m going to be completely irresponsible.”

The other line, “They are pieces of an intricate puzzle that may sometimes contribute to improved decisions”, is also (I believe) a truism. I wrote a little about mathematical predictive abilities in Addendum to “Minimizing Mistakecule Probabilities”. Predicting an absolute future is doable, yes, and not something I suggest doing with any regularity. Collapsing all those manifolds requires incredible amounts of energy.

Not sure what I’m writing about in that last paragraph?

Okay, let me explain it this way…

The ability to predict the future absolutely means Calvinism rules and there is no free will. I appreciate the appeal of this. It hearkens back to the ability to be completely irresponsible because you’re not responsible for anything, it’s all predetermined, and even your irresponsibility was predestined, therefore you’re not really being irresponsible, you’re being completely responsible by taking on the role of irresponsibility, …

And yes, I’m having fun with this. The number of errors in the above statements is amusing.

Ahem.

More seriously, please don’t relinquish your decision making authority to any tool claiming predictive capability beyond what can be reasonably and accurately measured.

These are the keys in both the article and the book; beyond what can be reasonably and accurately measured. You can increase the validity of your decisions via predictive analysis so long as you know, recognize and share the point where your decisions go beyond what can be reasonably and accurately measured.

Once you go beyond what can be reasonably and accurately measured, it all comes down to experience, intuition and luck, and to paraphrase the Apostle Paul, “The greatest of these is Luck.”

Example

Last night Susan and I were playing cards. About 1/4 of the way through the game she made a comment about her not winning and I smiled. I smiled because 1/4 of the way through the game it was completely obvious to me she was going to win, even though any “rational” observer of the game would claim I was greatly in the lead and had all the advantages.

How did I know she would win the game? Because I knew how many cards were in the decks (it was a two deck game), how many cards had been played, what cards had been played and the order they’d been played. The outcome, at that point in our play, was predetermined (barring mistakes neither of us often makes). In short, she’d have to play incredibly poorly to lose, and she never plays poorly.

I was able to predict the outcome, to use predictive analytics, because the system I was predicting was incredibly well defined, I could measure past and existing states with excellent accuracy and the number of possible outcomes was severely limited.

Predicting the future isn’t difficult to do. You just have to choose your manifolds carefully to conserve energy.

Have I ever mentioned that I’m not allowed in casinos? Not since the 1980s, anyway.

Surprised, huh?

NextStage Sentiment Analysis, Beta Test, Phase 2

First my thanks to everyone who took part in the Phase 1 Beta test of NextStage’s Sentiment Analysis (NSSA) Tool. This post covers modifications we made thanks to their comments and follows on Understanding and Using NextStage’s Level 1 Sentiment Analysis Tool.

Changes

  • Our developers installed the high-speed data system. Analyses that use to take 10 minutes now take about 60 seconds.
  • We added the Level 2 reports (beta testers will be seeing them in their outputs).
  • Level 2 users will also be able to download a XLS of the results (per Chris Berry’s request).
  • We modified two of the Level 1 reports.

First the newly added Level 2 reports.

Level 2 Reports

Rene suggested using The 10 Must Marketing Messages, Trust, Affinity, Author Rich Persona, Target Rich Persona and Worst Rich Persona.

Author Rich Persona

The Author Rich Persona report lists both the author’s Rich Persona and key elements of their {C,B/e,M} matrix. “{C,B/e,M}” is a shorthand notation for the Cognitive, Behavioral effective, Motivational matrix, a tool that calculates how an individual thinks about, responds to and is driven by any information in their environment. Knowing any individual’s or group’s {C,B/e,M} grants unprecedented knowledge of how to craft a message in order to generate a desired response or propagate a message to that individual or group (I can provide a long bibliography for those interested).

For example, a typical Author Rich Persona report looks like the following:


Author Rich Persona – This report will present the type of RP that has written the text (eg. V3) and a bulleted description of his characteristics.

This material was most likely written by an individual with a V14 Rich Persona. Key features of their {C,B/e,M} include:

  • These people are strongly motivated by what they see
  • They are success oriented
  • Presentations with emotions must be positive in nature
  • They make decisions based on what feels “right”, “correct” or “best”

Lastly, this individual probably falls into the following Myers-Briggs categories:
ISFJ, ISFP, INFP, ESFJ, ENTJ.


You can think of The Author RP Report as a kind of Me casa e su casa, meaning that people communicate best with those whose RPs and {C,B/e,M}s are identical to their own. The more identical, the easier the communication and the more easily shared complex cognitive and emotional concepts. Part of my training was learning how to shift my {C,B/e,M} at will to match those of people I was communicating with. Doing so enable me to better understand and respond to them, what is called establishing rapport.

So the above is telling you the author’s {C,B/e,M} casa. They will most effectively communicate with people whose casa is their casa. This is great if their {C,B/e,M} is the same or relatively close to the {C,B/e,M} of the largest possible population segment.

But if it’s not, then the most they can hope to immediately and directly engage is the population segment corresponding to their own {C,B/e,M} casa. They will capture the attention of population segments with {C,B/e,M}s close to their own and how much attention is captured (and then turned into engagement) depends on how psychographically distant the author’s {C,B/e,M} is from reader {C,B/e,M}s.

And before going any further, remember we’re just analyzing the Author’s RP. Including Target and Worst Rich Personae would have expanded that listing some 40 times! And without training?

Desired Intent and PsychoGraphic Desired Intent

Instead we’re offering a variant of some things Chris Berry requested in his original “Boy, if only I could find a Sentiment Analysis tool that did this” list , Desired Intent and Psychographic Desired Intent. Chris’ specific requests were:

click for larger imageWhat I came up with is the chart on the right (and it helps if you know some social mechanics. I can provide a bibliography if you’d like). The leftmost column indicates how much of the best audience will respond as the author desires. The center column indicates how much of the next best audience will get the message and respond. The rightmost column indicates how much of the worst audience will get the message and respond.

The concepts being used in these determinations involve psychological distance. The leftmost column indicates people in the target audience who think the way the author thinks, believes what they believe, learns the way they learn, decides the way they decide, …. all that exact-matching {C,B/e,M} stuff. The middle column can be likened to you listening to someone and responding that you think you agree with them and there’s a few things you need clarification on. The rightmost column can be likened to you listening to someone and disagreeing with them but not knowing why you disagree.

The 10 Must Marketing Messages

click for larger imageThis chart shows the relative intensities of ten messages that must be communicated in all media if the audience is going to positively respond.

I emphasize relative intensities because (my opinion) showing a scale of 0-100% doesn’t indicate how strongly a message was communicated, only that it had a certain intensity when compared to other messages. Normalization (such as scoring 0-100%) is useful in some metrics and not in this on (my opinion again). Someone may be communicating “I Can Help You” at 50points and let’s say that all other messages sum out such that the “I Can Help You” message is 50% of all messages being communicated. The next person is communicating the same message but for a different brand and their message is at 500points. Same other rules as above and it also sums out at 50%, but depending on lots of other factors that second message for the different brand wins because of its intensity, not because of how it normalizes when compared to all other messages. Currently NSSA produces normalized because I was out-voted. I’d love to hear your thoughts on this.

Also, I provide more examples of these ten messages in Reading Virtual Minds Vol. 1: Science and History.

Trust

click for larger imageTrust (for the purposes of this tool and thanks to Chris Berry) is defined as “the degree of trust between a person (brand) and a social network contained in the message”. What is being calculated is the author’s non-conscious belief that the audience will accept the message. A low score can indicate that the author doesn’t believe the audience will accept the message, that the author believes a small percentage of the audience will accept the message and so on. It doesn’t make much difference with high scores, you’re good any way you look at it.

Affinity

click for larger imageLevel 2 also includes an Affinity Graph (shown on the right). An author’s affinity to their audience is a measure of how much the author believes they are a member of their audience’s greater community. What’s particularly interesting about this chart is that it should not score high for people who also non-consciously think of themselves as either Influencers or GateKeepers because both functions indicate a non-conscious recognition of being separate (in some way, shape or from) from “the herd”. Author’s who score high as Hubs should score high in Affinity because the function of a Hub is to channel knowledge within a community, hence will have a greater self-concept of being a member of their own audience.

Changes to Level 1

Feedback and observing Level 1 users caused me to rethink some of the information in Level 1 and how it was displayed. The changes are to the Confidence (BS) meter and Message Retention Probability.

Message Retention Probability

click for larger imageThe Message Retention Probability chart originally showed two data points, how much of an audience will remember the message for 3 or so days and how much of an audience will be branded by the exposure.

Rene suggested I expand this to include some other options. What made sense (I’m open to suggestion on this) was a measure of how much of an audience will

  • Understand but Not Remember the message
  • Remember but Not Understand the message
  • Remember for 3 or so days
  • be Basically Branded

Each of the above are rough translations of how much of a message goes into what parts of the brain, long-term (“deep”) memory and cognition. The goal is to have the message lodge in both deep memory and the cognitive centers simultaneously, which is “basically branded”. Note that how large this value is depends a lot on who the intended audience is and how well written something is for that audience.

Suppose what is analyzed shows strongly in “Remember for 3 days or so”. Whatever the message is, it needs to be repeated inside that audience at least once a day for three days in order to shift things to “Basically Branded” (and remember, we’re not monitoring the audience, only the author. The audience would need to see the author’s message three times in three days to internalize the message). An analysis that shows strongly in “Remember but Not Understand” usually indicates that whatever the message is, it needs to be repeated through different channels. Lastly, “Understand but Not Remember” will normally take the lion’s share in any analysis. Note that that audience is not the audience for the message for any of several reasons, it’s simply the largest audience segment out there.

Confidence (BS) Meter

click for larger imageAs you can see, the Confidence (BS) Meter is now horizontal and clearly shows the 0 mark. Visually more informative with much less cognitive effort, I think.

Eating Our Own Dogfood Dept

Just for kicks, I ran the original version of this post through NSSA (sans blog interface, just the content). Can you say Ouch!.
So I went in and made edited. Four versions later, this post is what you get.
The differences are in the numbers:


V0 Version V4 Version

Love Factor

Positive 33.98 0.87
Neutral 1.01 98.76
Negative 65.01 0.36

Confidence

-72.32 -18.64

Message Retention Probability

Understand But Not Remember 21.46 19.63
Remember But Not Understand 0 0.25
3 Days or so 0 0
Basically Branded 0 0

Message Intent

Referral 22.55 25.81
Retribution 28.96 23.53
Love -1.54 18.3
Constructive 24.09 12.43
Troll 25.93 19.93

Author Influencer Type

Influencer 28.57 62.41
GateKeeper 63.23 37.59
Hub 8.2 0

10 Must Marketing Messages

We Trust You 8.19 10.23
You Can Trust Us 18.21 17.02
This Is Important 2.17 1.26
This Is Important to You 7.16 6.63
We Can Help 9.24 12.12
We Can Help You 24.72 20.86
You Are Good People 8.24 8.41
We Are Good People 7.53 8.86
They Are Not Good People 6.97 5.68
We Are A Leader 7.57 8.92

Trust

0 10.00935

Affinity

0 7.732702

Author Rich Persona

A15 V1
MB: ESTP No MB

Desired Intent and PsychoGraphic Desired Intent

Desired Intent (First Circle)- A15 71.72 (V1) 29.97
PsychoGraphic Desired Intent (Outer Circle) – A9,A10,A11,A12,A13,A14,A15,A16 4.95 (V2 ,V1 ,V3 ,V4 ,V5 ,V6 ,V7 ,V8) 2.03
PsychoGraphic Desired Intent (Outmost Circle) – K23,A7 ,K7,V23,A15,A23,K15,V7 ,V15 0 (A1 ,A9 ,K1 ,A17,K9 ,K17,V1 ,V9,V17) 0

Major changes through the revisions were removal of massive bibliographies, caveating, general de-sciencing of the content (I can email the V0 post to any insomniacs with a need). Of particular note is the big change in Desired Intent. The First Circle value scored about half the V0 version of this file. Why? Because I was shifting my {C,B/e,M} from an A15 to a V1 {C,B/e,M} (ResearcherJoseph to BusinessBloggerJoseph). This is a tip of the hat to long time editor Brother Brad Berens who’s been telling me to do the same for years now.

Summarizing….

Beta testers will once again be turned loose by the time this post goes live.

Enjoy and please let me know your thoughts. Tools evolve through use and interaction, and as I explained in Eight Rules for Good Trainings (Rules 1-3) and Eight Rules for Good Trainings (Rules 4-8), I learn from others more (I’m sure) than they may ever learn from me. Example: One of our beta testers is a fellow in his early 20s. My reasoning for including him? Whatever else he does during the day, his interests are going to be very different from mine. He’ll put material through analysis that I don’t even know exists.

Again, thanks and enjoy.

Google v China or “Rollerball Redux”

Google and China are involved in an information war. Not a PR thing, they are battling in the ultimate arena; exchange.

People who’ve read Reading Virtual Minds Vol. 1: Science and History and my various blog posts know about fair-exchange, information commerce and the like. Basically whatever goods or services are bought, sold, bartered, …, it all comes down to an exchange of information (from a pure social-semiotic perspective).

If you’re watching carefully I think you’ll notice that this is the most recent (note, most recent, not the first although the first I know of in this century) demonstration of a company, a business, challenging a country for information (again remembering/recognizing that “information” is the ultimate exchange). This is not the metaphorically unwinnable “land war in Asia”, this is for the ultimately unwinnable control of “what people know and how they get what they know”

James Caan in the original RollerballAnybody remember the original James Caan “Rollerball”? Governments were gone or ineffectual and companies ruled the world. The “opiate of the masses” wasn’t Marx’s religion, it was the hyperviolent Rollerball.

And if Google really is capturing all that data about people that the anxious out there believe they are gathering, hyperviolence is going to be the least of our worries.

I mean, have you played some of those xBox games lately?

Questions for my Readers

I’m having a go-round with myself about making my writing better and more accessible (in all my blogs) to readers. I’m hoping that readers will give me some feedback on some items so I can improve.

Please feel free to give me feedback as comments here, via Skype (nseJDC), email or phone calls. I’m pretty reachable and quite willing to learn.

  • Is my writing friendly or unfriendly?
  • Do people find my use of faux-HTML (things like <CAVEAT>, etc.) irritating?
  • Is my writing just plain difficult to read?
  • Is the use of images in my blog posts distracting, helpful, …?
  • Are my posts just plain too long?
  • Is my writing narcissistic?
  • Do I quote myself too much?
  • Do I concern myself too much with scientific accuracy in my writing?
  • And lastly: Do I need to share more about myself?

I’m genuinely curious, folks, and would appreciate any feedback I can get.

And if there’s something I can do to improve my writing that isn’t listed in the above, please let me know that, too.

Thanks. – Joseph

Understanding and Using NextStage’s Level 1 Sentiment Analysis Tool

For those of you who weren’t in the loop, NextStage has been taking it’s desktop tools and turning them into web tools. The first to come out of that particular shoot is NextStage’s Sentiment Analysis Tool. I’ve written about that tool before in Sentiment Analysis, Anyone? (Part 1) and Canoeing with Stephane (Sentiment Analysis, Anyone? (Part 2)). Here I’ll be sharing how to use and understand the Level 1 version of that tool.

NextStage’s Level 1 Sentiment Analysis tool provides the following information (per Rene):

  • Love Factor – This report will provide an horizontal histogram composed of 3 items: Positive, Neutral and Negative. It will thus show on a scale from 0 to 100 the positive, neutral, or negative degree of the message. As in real life things arent black or white we expect that any message will score in the three dimensions but at different levels.
  • BS Meter – This report will present a gauge with a scale from -100 to 100 with shade of colors ranging from red to yellow and to green. It will present if the author of the analyzed text believes (actually, whether or not the author is confident in what they’re writing) or not what he has written and to what extent. We believe that a score over 25 means that the author believes what hes written and under -25 the opposite. In between, the author is not really sure
  • Message retention Probability – This report will present in a gauge in a scale from 0 to 100 the probability that the conveyed message will be retained by the readers of this message. It is stated as a probability as this will depend upon the type of visitor reading the text (its Rich Persona) and it will be reported against the whole population of our Synthetic Users.
  • Message intent – This report will provide an histogram composed of 5 items: Referral, Retribution, Love, Constructive and Troll. It will thus show on a scale from 0 to 100 the intention of the message for each of these items. The same consideration as for the first report applies (non Black or White).
  • Author Influence type – This report will provide an histogram composed of 3 items: Influencer, Gatekeeper and Hub. The scale will go from 0 to 100 and will present the type of author that has written the text.

As always, I’ll use my own writings for demonstration purposes in the beginning. The reasons for this are simple:

  1. the NextStage Sentiment Analysis tools are reporting on the non-conscious of the author when that author was composing the information.
  2. I’ve had several dozens of years of training to recognize, understand and report on my own non-conscious activities and behaviors hence will be able to describe whether or not I believe what NextStage’s Sentiment Analysis tools are reporting.

I’ll analyze some other online material (again, for demonstration purposes) once I’ve analyzed a few of my own (let me know if you’d like something you’ve written analyzed).

You Found It!

Our first lookLet’s start with the first Triquatrotritecale post, You found it!. What is there now isn’t what we original had (the original is shown on the right and we’re still working on it). But what did that original post have to say about me when I wrote that post? What say we find out.

  • Neutral meLove Factor – First you need to know that I’m a stickler for accuracy. I’m very uncomfortable with (what I call) marketing truth, the tendency of people to put their spin on things so that what is horrible doesn’t sound so. Example: President Obama’s stating that the Copenhagen accord was “Meaningful and unprecedented.” Both are true and I’ll concede that both are accurate. I’ll also offer that neither are accurate truths. It’s kind of like a sin of omission mixed with caveat emptor and some it’s what you don’t know that’ll hurt you thrown in. One of our NextStageologists chides me that I’ve become very good at marketing-speak and it wounds me, truly. Or accurately.Accuracy versus truth comes into play with the Love Factor chart. You see that the Neutral reading is high compared to Positive and Negative, yes? That’s true and not accurate. Human beings are not naturally “neutral” to much of anything, not non-consciously anyway. What NextStage’s Evolution Technology (ET) really recognized was that my Positive reading was about 52 points and my Negative was about 48 points. The truth of this is that I was working at being neutral. To most people it would come off as neutral and only because the conscious brain takes the non-conscious information “He’s working at being neutral” and mentates “He’s neutral”.

    My position (ahem) is that there’s a lot of difference between someone working at being neutral and truly being neutral. Susan and Charles disagreed with me. More accurately, they agreed with me and also offered that the subtlety would be lost on most people (I think better of you, dear readers, than do they).

    The accurate truth is that my Positive value was 210, my Negative was 192.5, my Neutral somewhere around 20. These values indicate that while I was working at being neutral, I wasn’t really busting my gut over it, more like I was just another human being being human.

    But in any case, NextStage’s Sentiment Analysis tools will report something like you see above unless the Positive and Negative values are “arithmetically” different, meaning the author recognizably writes one way or the other.

  • BS MeterBS Meter – First, I’ve never been comfortable with the term “BS” or any of its variants. Also, what NextStage’s Sentiment Analysis tools measure is whether or not the author non-consciously believes (“accepts” is a more accurate term) what they’re offering as being valid information. Most accurately, ET takes a reading of whether or not the author is confident in the information they’re providing then matches that to some other things and the result is a measure of their confidence in what they’re writing.The distinction may seem subtle and I assure you it’s not. That distinction is shown on the above chart. What I wrote in You Found It! was quite true and accurate.

    But (!!!) I really didn’t have any idea where this blog would go or what I would post about when I wrote You Found It!, hence my confidence in what I was writing wasn’t as good as it could have been. For that matter, I wrote in I’m the Intersection of Four Statements “I consider myself one of the least confident people I know.” so my confidence levels should never be incredibly high.

    What can be gleaned from this metric is that when the author (yours truly) wrote that post they definitely were uncomfortable with the information they were presenting. The value (-52.81) indicates a probable lack of confidence in what they were presenting. Was it BS, though? I suppose that depends on what the person reading the charts thinks of the author. Neutralizing that “person reading the charts” bias is a lot of what NextStage trainings are all about (just an FYI, folks).

  • It's like water, except it's a message, and as you grow older you retain more.Message Retention Probability – This is another metric which can be true, accurate or both. This image is about as true and accurate as I can mathematize them. What’s showing is this: Taking the greatest population swath possible, basically 0% will remember what I’ve written.This is both true and accurate. Especially if you know what’s being calculated.

    Knowing only what is written and nothing about who is reading, the writing style I use and topics I write on suit such as small audience that when measured against the entire blog-reading population, I basically write for 0% of the population (the other truth involved in this is that most people don’t know how to write or design information for the largest possible audience. This more than anything else is why most websites are thrilled to get 3% conversions, why companies have to practically hit you over the head before you can remember their brand and act on it in any meaningful way, …).

    However, my regular readership retains about 90% of what I write. This isn’t to suggest that everything I write is understandable, only that it’s easily memorable.

    So, if the question is “what percentage of the general population will be remember what I’ve written?” the answer is 0%. If the question is “what percentage of my readership will retain what I’ve written?” the answer is 89.25%.

    The only way I know of to answer that last question, though, is if you have NextStage’s OnSite tool tracking your site (that’s a description of the limits of my knowledge, not a plug for NextStage technology).

    So we can resort to something that is either true or accurate and both depend on how fine you want to cut things. For example, if you’re not interested in the tightest possible segmentation, my writing (or at least that one post) would be memorable to just under 17% of the general population. That’s true but not accurate because of how the brain retains information. Information (such as a webpage) may be presented visually (for example) and it will not necessarily be remembered (think of how much you see in a day — heck, in a minute — that you can remember seeing three days — heck, three hours — later). What has to occur is that the information be presented in a way that is both stimulating (to lock attention on it) and memorable in the way that the greatest percentage of the population remembers information.

    Or presented as you know your audience will remember it (read “configured so that both brain and mind assign high enough “survival” value on the information that said information is quickly placed in deep or ‘long term’ memory”).

    So the question becomes, do people using NextStage’s Sentiment Analysis tools want things true, accurate or both. We can do them all…oh heck, why not just do them all and have done with it?

    By the time we release these tools to the general public this particular report will provide true, accurate and both true and accurate results. Let me go do that now, in fact…

    (about half an hour goes by)

    Okay. This report will now (“now” meaning as soon as our programmers convert my math into working code. They’re very good at it. It’ll take them less time to get it installed and working than it took me to mathematize it) show “Understand But Not Remember”, “Remember But Not Understand”, “3 Days or so” and “Basically Branded”.

    (and I hope you all appreciate what I do for you)

  • Who loves ya, baby?Message Intent – This, thank goodness, is a fairly straightforward report to apply. What is shown here is that a) your author is pretty mild-mannered over all (the numbers are kind of equal) and b) wanted to get back at someone or some thing (the Retribution value). In this case, it was the KMM blog platform and for reasons I made obvious in both You found it! and Today I was asked if I was comfortable doing NeuroEngineering. The Referral, Love and Constructive values being pretty close to each other hearkens back to the “working at being neutral” versus “being neutral” thing mentioned above.
  • And what do I think about me?Author Influence Type – This is a metric that one needs to understand clearly. NextStage’s Sentiment Analysis Tools are metricizing whether or not and how much the author believes they are an Influencer, a GateKeeper and a Hub. This is not an indication of how their readership thinks of them, only how the author thinks of themself (learning what an author’s readership thinks of the author would require NextStage’s OnSite tool or something similar).The results for this post did surprise me (and remember, this is a non-conscious metricizing of myself). Consciously I don’t think of myself as either influencer or hub. I would accept having a GateKeeper mentality and recognize that would be my boundaries and limits kicking in (“boundaries and limits” as in personal boundaries and personal limits. Most everyone who knows me tells me mine are incredibly strong).

    But then I thought about it. According to Twitter I’m either influential or highly influential. This information amazed and baffled me. People take me seriously? People think I know what I’m doing? Wow.

    Even so, not knowing how Twitter comes up with such definitions I had trouble accepting it (although it was flattering). But then several conversations over the past months revealed what some people are calling “The Joseph Effect”. People want to emulate my methods and principles in their lives (very flattering). One person told me that they were actively incorporating The Joseph Effect in their life and the change has been recognized by others as both growth and positive. Okay, more than one person made such a comment to me. Several, in fact.

    This is truly incredible to me. Get to know me better and your attitudes will change, I’m sure.

    However, all that stuff had obviously been roiling in my non-conscious for a while. Whether I consciously accept it or not, I non-consciously recognize that I influence people.

    And then I remembered debating with myself for a good hour or so whether or not to make “public” that Susan and I had donated to what I consider a good cause. This was a real debate for me, the intersection of “Let your light so shine (don’t hide your light under a bushel)” and “Don’t let one hand know what the other is doing”. I finally decided that publishing our involvement might cause others to become involved hence I had concluded I was an influencer even though there was no conscious recognition of “I’m an influencer”.

    Le coeur a ses raisons que la raison ne connait pas. (Pascal)

    And no scarier a thought could one have on a sunny Tuesday afternoon.

    But what about what I wrote, forgetting the webpage part?

    I demonstrated in Sentiment Analysis, Anyone? (Part 1) that there’s a difference in what someone writes/designs and how all the blah-blah of the web interface shows people. Think of it as an equation:

    Informationwritten + Informationweb interface = Total Presentation

    The above was all for the Total Presentation shown in the webpage snapshot I shared close to the beginning of this post. What’s the Sentiment Analysis for just the written text?

    More truthFor one thing, once the mitigating influence of the page interface is removed, my truer feelings reveal themselves. Why such a substantial difference with the interface and without? Because I used colors and phrases (for the right column in the blog) that tend towards neutrality rather than offense or defense.

    That brings us to the Confidence (BS) meter. I won’t bore you with another picture. The original was -52 and change. The pure text value was -51. No matter how you cut it, I’m a very cautious person.

    Pretty much the previous result holds true for the revised Message Retention Probability metric. The general public will not understand nor remember what I’ve written for any period of time. Remember, that’s the general public. Perhaps we need to include the option of the tool user entering an audience from a pick list? Rene? Anyone? Of course, that might merely prove that not only can the author not write, the person running the test has no clue of what audience the author is targeting. On the other hand, if you’re in charge of marketing for a company blog, you’d have a great idea of who the audience is.

    This would be incredibly useful in determining who’d be best suited to write content. Take the naked webpage and plug in some content (3-4 pieces should be enough) from as many authors as you like. Run a test on each set. The author that scores the best with the desired audience is the one who should be writing your content.

    Yeah, I like that.

    And what I really meant was...Next on the list is Message Intent. Here I show both “Total Presentation” and “Just the Text” values side by side. It’s intuitively obvious to the casual observer and a well known fact among all my regular readers that I love you I love you I love you and it doesn’t matter whether an interface is used or not. In the case of this post, I care about each and every one of you equally (ahem).

    Equally interesting is the rise in Retribution. Strip away the interface and I was one unhappy camper.

    This is more accurately how I think of myselfAnd when you strip away all the artifice of the interface? As I wrote earlier, I’m a GateKeeper. Anybody who’s asked me to share someone else’s personal, private or similar information knows NextStage Principle #51 takes affect.

    Okay, enough for now. I know there are beta testers waiting to play. If you haven’t heard from Rene or me yet you will in a few days. Or email me or Rene and let us know you’d like to play.

    Next time out, an analysis of some other folks’ blog posts (let me know if you’d rather I not analyze your blog).

    And before I forget

    I’m writing this post because of my firm belief that people need training when encountering new tools (at least I require training…”require”? I actively seek it out). Susan suggests a mindset of “We’re not in Kansas anymore” crossed with Friendship Bread when using NextStage tools because our tools measure things that go “bump in the night” as far as most people are concerned and definitely are different from clicks, pageviews, cookies, …

    <SUSANISM>
    Have no fear NextStage will offer plenty of training opportunities and lots material when the next level is ready.
    </SUSANISM>


Posted in NextStageology, Sentiment AnalysisTagged , , , 14 Comments

About Me

Joseph Carrabis is Founder and CRO of The NextStage Companies (NextStage Evolution, NextStage Global and NextStage Analytics), companies that specialize in helping clients improve their marketing efforts and understand customer behavior. You can reach NextStage Evolution at 603 791 4925 or via info(at)nextstagevolution(dot)com.

Carrabis has authored 25 books (most recently Reading Virtual Minds Volume I: Science and History) and over 500 articles in five areas of expertise. His books have covered cultural anthropology, database technology and methods, information mechanics, language acquisition, learning and education theory, mathematics, social network topologies, and psycholinguistic modeling. His articles have covered computer technology, cultural-knowledge modeling, equine management, knowledge studies and applications, library science, martial arts, myth and folklore, neurolinguistic, psychodynamic and psychosocial modeling, group and tribal behavior, and social interactions in NYC and more. His writings are available at AllBusiness.com, An Economy of Meanings, BizMediaScience, iMediaConnections, Politics2012, Stating the Obvious, That Think You Do, TheAnalyticsEcology and Triquatrotritecale.

Carrabis is a Senior Research Fellow at USC Annenberg’s Center for the Digital Future, Senior Research Fellow and Board Advisory Member for the Society for New Communications Research and served as Founder, Senior Researcher and Director of Predictive Analytics for the Center for Semantic Excellence. He is a member of Scientists Without Borders, the AAAS’ Section Z, the Association for Psychological Science and the New York Academy of Sciences. He was selected as an International Ambassador for Psychological Science in 2010 specializing in trauma and AIDS therapies.

Carrabis has been a lead speaker, guest presenter and panelist at several industry, trade and academic conferences and conventions, ranging from The MIT Enterprise Forum to the International Communications Association Conference on the scientific side and from the eMetrics Summits to XChange to iMedia Summits on the business side. He is invited to present at scientific conferences and contribute to journals more frequently than time allows.

Carrabis has been awarded patents for NextStage’s Evolution Technology, a broad series of patents creating a new field of technology and applications. Evolution Technology allows any programmable device to understand human thought and respond accordingly.