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Your Project Notes
Interpret graph data, save ideas, AI content, and analytics reports.
Auto-Generate from Analytics
Top keywords (global influence):
Top topics (local contexts):
Explore the main topics and terms outlined above or see them in the excerpts from this text below.
See the relevant data in context: click here to show the excerpts from this text that contain these topics below.
Tip: use the form below to save the most relevant keywords for this search query. Or start writing your content and see how it relates to the existing search queries and results.
Tip: here are the keyword queries that people search for but don't actually find in the search results.
What is your expertise, what is it that you are selling. Are you selling a product or a service, then you are an entrepreneur. Are you selling money and buying equity, then you are an investor. Are you selling your expertise, and your time, and you are a consultant. Maybe there are other ways of providing value in the ecosystem as well. But these are three fairly well understood ways of going about measuring generating and deploying what you have and what others might need.
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So, I have had an experience in two of these. And now I am testing the third. I have never been a consultant. I have never sold my time or expertise. But recently, I decided why not. And I'm doing various kinds of experiments. So, for example, I receive a lot of pitches for people who are looking for investment. And of course, together with my team we analyze, which are the best. We invite those who are ready, in our opinion to pitch. And recently, you may have noticed, we turned. These pitches. Not all of them but many of them to be live and Network Society Pitching Live, which is alive on Pitching.live as a website is a fun way of making these sessions, Interactive, an opportunity to learn for many other teams. How to page, what mistakes maybe to avoid.
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And it is also a great way for the team who comes on board to make themselves known. And of course, It gives me the opportunity to ask the questions that I would be asking anyway. So, I decided to also allow teams that maybe are not ready for pitching yet for whatever reason to book a pitching review or project review session with me. And it is really wonderful to see how smooth. The process can be thanks to tools that are available immediately and to anybody. Rather than having a lot of friction in receiving the page booking the appointment, getting paid, and so on. The whole thing is set up with just putting various tools together. So, the pitch deck can be sent via email or uploaded in a form. The appointment can be booked via calendly. And the payment for the time that is booked can be immediately made via a credit card. So, the form is integrated and everything really works very well. I am also offering a money back guarantee on the 90 minutes session where 30 minutes. The
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project is presented and described 30 minutes, I give feedback. And then 30 minutes, we discuss what can be done what are the next steps and things like that. And, of course, this can be adjusted as needed. The session is recorded transcribed and the project receives the recording and the transcription, as part of the value that is delivered to them. They can come, or just one person, it is of course recommended that the CEO is the one delivering the, the pitch or describes the, the project, but up to five people can join in the same session. so that many more points of view can contribute to making it really valuable for them.
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And then, and once again. I really want to make this easy for anybody to accept and to go with it. And that is why I am offering this 30 days, no questions asked. Money Back Guarantee. I said 30 days it's actually not 30 days whenever whatever doesn't matter. And the 30 days money back guarantee is a standard right in retail and in many other ways mail order online, if you buy something from Amazon, you can return it.
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I think it is a great way for expressing appreciation for the business that you are receiving on one hand, and confidence in the value that you are providing, on the other hand, many years ago when I started one of my first businesses, it was still in packaged software, right, and packaged software was sold in boxes and the box would contain either a floppy disk at the beginning or the CD. Later on, and there would be quite an obsession, about piracy and winter, people would abuse. The software license in some way. And I remember that at the time in the 90s. In Italy, I introduced something like the first time, and no questions asked. Money Back Guarantee. On the software that was purchased, whether directly from my firm or through the distribution channels in the stores. And this was unheard of. To the point that the stores had an official policy, not to take back any software package that was open. You could only bring them back unopened software. So I guess, even though they are
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required by law to have either seven days or maybe already 30 days, is that you would go home.
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And then you would have buyer's remorse, and you would decide that you shouldn't have bought. What you bought, and you would bring it back. But in the meantime, you would just put it on the shelf and contemplate it, you would not touch it. Well, isn't that crazy isn't it pointless. Also, software piracy was easy. It is easy, and it should be easy to find software. And to start using that software. Today we call it Freemium, you start using something for free with certain features. And then you like it's so much and you want more features that you start paying for it. At the time, it could have been called shareware or trial where, where you could download the software and use it for 30 days, and then you were supposed to start paying for it if you kept using it.
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But
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why would it be a problem to do the reverse that you buy the software you pay for it. And then if you decide that you want to stop using it, you get your money back. The classical objection is that. Oh, it will be abused, people would ask for their money back, but still keep using the software that they the, at that point, don't have license for they didn't pay for. In my opinion was at the time and it still is that it is worth trusting your client that is worth trusting the customer, because it is a beautiful way of opening yourself up to a new relationship. And this is, this is true. I would say most of the time, if not all the time, not only in sales, but in life. And yes, there will be those that maybe abuse it, but you learn from them as much or maybe more you receive value through that.
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That
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broken relationship will teach you how to adapt and maybe how to deliver more value, so that it is impossible to abuse because the continued availability, for example, of future value is going to be the one that guarantees that nobody will want to break it. And that was the experience, back then 30 years ago, practically, nobody. Not one in 100 not one in 1000 not one in 10,000 word abuse. This kind of thing. A, and when somebody really wanted to get their money back, we would do everything possible to make sure that they got their money back, whether it was $50 or $100, or 5000, or $1,000, there were those expensive software packages in retail stores too.
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And
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sometimes we had to ask the customer to please go to the retail store and then have them call us so that we would confirm that yes they could refund the customer, and then in turn the retail store would be covered by this guarantee and they wouldn't lose money. Eighth was great. So, based on that experience. I fully believe that I can apply the same today in this kind of project review and pitch review service that I am offering. Two startups and two projects. So, I am experimenting in, and I am very curious to how it is gonna go what is going to happen. and then iterating and maybe scaling. Except that, of course, the traditional consulting business is kind of the opposite of scalable, because your time is measured by the money you receive. So you kind of tell your client. Oh, by the way, I am going to deliver the value rather than in 90 minutes in 10 minutes but please pay me the same. So, it will be interesting to see what this is going to mean, I do believe in scanning. I do
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believe in automation, I do believe in the fan keep delivering higher and higher levels of value and justify an increasing economic relationship, rather than a decreasing economic relationship. Not everybody believes that this is possible. I was, for example, astonished to learn recently that Upwork kind of threw in the towel Upwork, which many of you will be familiar with is a pretty good platform for finding freelancers, either directly or through agencies, and to engage with them from copywriting to code coding developing applications for mobile or or
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personal
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computers to designing logos or designing entire advertising campaigns. And you have to go through the process of being able to articulate what you want. And of course that is in itself a very good exercise because it is a very common experience for consultants that there is a deep misunderstanding between them and the client of what the client is is describing what the consultant is understanding and so on. There are even, cartoons about the ridiculous levels of misunderstanding that this can imply. And then the second part is that if you complete the jobs job description, and you post it. You will be flooded
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with with offers. And I used it many times and it is a common experience that you describe a job as the morning after you have hundreds of candidates. Ready to compete for the job. And some of them will be very easy and very fast to be discarded because they are robotically apply and without even having understood, or maybe even having read what the requirements were others will be very good matches. And then of course you will have to decide, do I pay more, because I believe I will get more value or do I pay less. And then there is an interview process with the final candidates. And there can be some tests. For example, I like to run a paid test. If the engagement is continuous, then you can reasonably say to the candidate Listen, why don't we do a test I will pay you for it. And then based on that we will be able to better understand if this thing is going to work. For example, copywriting can be done like this, and then you will know if the person writes in the kind of tone that
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you want. If you will be able to really rely on the person doing any kind of research that is needed.
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The
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final step of course is to decide who you want to hire for that particle job, and then work with them. Assign milestones, pay them regularly and the Upwork platform supports all these and many other processes as well. Now, what I didn't know. And I discovered recently is that in order to not have to discover and understand how freelance consultants in freelance developers and copywriters and logo designers and all of that. How can they add value in a globally connected marketplace where there is a clear and intense competition from Pakistan and India and China. With thousands and 10s of thousands of eager and qualified candidates for every job. Compared to much more expensive freelancers in the US.
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They
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separated the marketplace. It is not a global marketplace anymore. You can list, jobs that are us only in a kind of a protectionist perversion of what should have been a global marketplace. And that us only job is going to be paid at us rates, and only us locations can apply. It doesn't matter. For example, if you are a US person living abroad, and you are a US citizen. You cannot apply, you have to be in the US reside in the US. And, and then you can you can apply. No, in my opinion, this is a huge defeat. It is a defeat for the marketplace, but it is also a defeat for us freelancers who are weakened by this kind of protection, because they are not forced to understand what kind of value they can add the global competition is a reality. And it is not going to go away, because this competition is, is there or because of this protection, being there.
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So,
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I am not planning to do that I am not planning to diversify my rates and make it more expensive for the US less expensive for for India or China. What I am planning to do is to search for value, keep searching for value. What can this be well, for example the recording the transcription of these meetings, and under easy value that you know because you have been following these episodes of The context is the topic, analysis and the chart, and other will be the collection of every reference and URL and source and report and PDF that comes up during that meeting, or further, and so on and so forth. There will be many others, I am sure. So, these experiments are fun. I am looking forward to engage with interesting projects that recognize the value, and maybe they see it as a stepping stone for me to become an advisor or a mentor, and maybe also an investor and. Is it a traditional kind of approach that you would be paying the investor before they start investing. No, not at all. Will some
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be incense and and will they absolutely categorically refuse this kind of relationship. Totally. And that's fine. A bottle of course, there can be many interesting things that are being born from it. Anyway, so that is why I'm going to do the experiment and maybe update you in one of the future episodes of the context, to see how it is going. In the meantime, if you haven't become a supporter on Patreon. This is a time to go and check it out. There are four levels to become a member of my Patreon community, a fan, a supporter, a sponsor and a benefactor. And these are at different levels of economic commitment, and I articulate on Patreon and you can read it in detail the differences of how I share content with you. I share my attention with you. I share my knowledge with you in I create for you. These are the different things that happened, I had the different levels of mutual engagement and mutual relationship. So, let me know. What do you think about that as well. And see you at
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the next episode. Oh, another thing. You may notice that the background is different. I am in front of a green screen. I had this green screen for a long time but I didn't end up using it for many reasons. My team and I are thinking about the next season of The Context, and how to upgrade the production quality and the production values of the context. And this is part of that. A. And what you see is not yet, the graphical design and all the identity and all the value that we are planning to put into season three. But it is a hint in the direction where we are going. So, this is the last thing that comes into my mind and see you next week.
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Semantic Variability Score
— modulates diversity of the discourse network how it works? The score is calculated based on how modular the structure of the graph is (> 0.4 means the clusters are distinct and separate from one another = multiple perspectives). It also takes into account how the most influential nodes are dispersed among those clusters (higher % = lower concentration of power in a particular cluster).
Actionable Insight:
N/A
We distinguish 4 states of variability in your discourse. We recommend that a well-formed discourse should go through every stage during its evolution (in several iterations).
1 - (bottom left quadrant) — biased — low variability, low diversity, one central idea (genesis and introduction stage).
2 - (top right) - focused - medium variability and diversity, several concepts form a cluster (coherent communication stage).
3 - (bottom right) - diversified — there are several distinct clusters of main ideas present in text, which interact on the global level but maintain specificity (optimization and reflection stage).
4 - (left top) — dispersed — very high variability — there are disjointed bits and pieces of unrelated ideas, which can be used to construct new ideas (creative reformulation stage).
Read more in the cognitive variability help article.
1 - (bottom left quadrant) — biased — low variability, low diversity, one central idea (genesis and introduction stage).
2 - (top right) - focused - medium variability and diversity, several concepts form a cluster (coherent communication stage).
3 - (bottom right) - diversified — there are several distinct clusters of main ideas present in text, which interact on the global level but maintain specificity (optimization and reflection stage).
4 - (left top) — dispersed — very high variability — there are disjointed bits and pieces of unrelated ideas, which can be used to construct new ideas (creative reformulation stage).
Read more in the cognitive variability help article.
Shows to what extent you explored all the different states of the graph, from uniform and regular to fractal and complex. Read more in the cognitive variability help article.
You can increase the score by adding content into the graph (your own and AI-generated), as well as removing the nodes from the graph to reveal latent topics and hidden patterns.
You can increase the score by adding content into the graph (your own and AI-generated), as well as removing the nodes from the graph to reveal latent topics and hidden patterns.
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Download: TXT Report CSV Report More Options
We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.
positive: | negative: | neutral:
⤓ ? ⤓
Use the Bert AI model for English, Dutch, German, French, Spanish and Italian to get more precise results (slower). Standard model is faster, works for English only, is less precise, and is based on a fixed AFINN dictionary.
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Main Topical Clusters & High-Level Ideas
? The topical clusters are comprised of the nodes (words) that tend to co-occur together in the same context (next to each other).
We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph using the Force Atlas algorithm (Jacomy et al) and are given a distinct color.
We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph using the Force Atlas algorithm (Jacomy et al) and are given a distinct color.
please, add your data to display the stats...
+ ⤓ ⤓ Most Influential Keywords & Concepts
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The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.
We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.
Click the Reveal Underlying Ideas button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.
We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.
Click the Reveal Underlying Ideas button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.
please, add your data to display the stats...
+ ⤓
Network Structure:
N/A
?The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.
Download: TXT Report CSV Report More Options
Structural Gap Insight
(topics to connect) ?A structural gap shows the two distinct communities (clusters of words) in this graph that are important, but not yet connected. That's where the new potential and innovative ideas may reside.
This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.
This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.
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Discourse Entrance Points
(concepts with the highest influence / frequency ratio) ?
These nodes have unusually high rate of influence (betweenness centrality) to their frequency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.
These are usually effective entrance points into the discourse, as they link different topics together and have high inlfuence, but not too many connections, which makes them more accessible.
These are usually effective entrance points into the discourse, as they link different topics together and have high inlfuence, but not too many connections, which makes them more accessible.
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Emerging Keywords
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Evolution of Topics
(number of occurrences per text segment) ?
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The chart shows how the main topics and the most influential keywords evolved over time. X-axis: time period (split into 10% blocks). Y-axis: cumulative number of occurrences.
Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).
Drag the slider to see how the narrative evolved over time. Select the checkbox to recalculate the metrics at every step (slower, but more precise).
Main Topics
(according to Latent Dirichlet Allocation):loading...
⤓ ? ⤓LDA stands for Latent Dirichlet Allocation — it is a topic modelling algorithm based on calculating the maximum probability of the terms' co-occurrence in a particular text or a corpus.
We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
We provide this data for you to be able to estimate the precision of the default InfraNodus topic modeling method based on text network analysis.
Most Influential Words
(main topics and words according to LDA):loading...
We provide LDA stats for comparison purposes only. It works with English-language texts at the moment. More languages are coming soon, subscribe @noduslabs to be informed.
Sentiment Analysis
positive: | negative: | neutral:
⤓ ? ⤓
We analyze the sentiment of each statement to see whether it's positive, negative, or neutral. You can filter the statements by sentiment (clicking above) and see what kind of topics correlate with every mood.
The approach is based on AFINN and Emoji Sentiment Ranking
The approach is based on AFINN and Emoji Sentiment Ranking
Use the Bert AI model for English, Dutch, German, French, Spanish and Italian to get more precise results (slower). Standard model is faster, works for English only, is less precise, and is based on a fixed AFINN dictionary.
Concept Relation Analysis:
please, select the node(s) on the graph or in the table below to see their connections...
+ ?
Top Relations in 4-grams
(bidirectional, for directional bigrams see the CSV table below):
The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default. Occurrences shows the number of the times a relationship appears in a 4-gram window. Weight shows the weight of that relation.
As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).
As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).
Text Statistics:
| Word Count | Unique Lemmas | Characters | Lemmas Density |
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Text Network Statistics:
The higher is the network's structure diversity and the higher is the alpha in the influence propagation score, the higher is its mind-viral immunity — that is, such network will be more resilient and adaptive than a less diverse one.
In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.
We recommend to try to increase mind-viral immunity for texts that have a low score and to decrease it for texts that have a high score. This ensures that your discourse will be open, but not dispersed.
In case of a discourse network, high mind-viral immunity means that the text proposes multiple points of view and propagates its influence using both highly influential concepts and smaller, secondary topics.
We recommend to try to increase mind-viral immunity for texts that have a low score and to decrease it for texts that have a high score. This ensures that your discourse will be open, but not dispersed.
The higher is the diversity, the more distinct communities (topics) there are in this network, the more likely it will be pluralist.
The network structure indicates the level of its diversity. It is based on the modularity measure (>0.4 for medium, >0.65 for high modularity, measured with Louvain (Blondel et al 2008) community detection algorithm) in combination with the measure of influence distribution (the entropy of the top nodes' distribution among the top clusters), as well as the the percentage of nodes in the top community.
We recommend to aim for Diversified structure if you're in the Biased or Focused score range and to aim for the Focused structure if you're in the Dispersed score range.
We recommend to aim for Diversified structure if you're in the Biased or Focused score range and to aim for the Focused structure if you're in the Dispersed score range.
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Discourse Advice:
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Narrative Influence Propagation:
? The chart above shows how influence propagates through the network. X-axis: lemma to lemma step (narrative chronology). Y-axis: change of influence.
The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).
The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
The more even and rhythmical this propagation is, the stronger is the central idea or agenda (see alpha exponent below ~ 0.5 or less).
The more variability can be seen in the propagation profile, the less is the reliance on the main concepts (agenda), the stronger is the role of secondary topical clusters in the narrative.
We plot the narrative as a time series of influence (using the words' betweenness score). We then apply detrended fluctuation analysis to identify fractality of this time series, plotting the log2 scales (x) to the log2 of accumulated fluctuations (y). If the resulting loglog relation can be approximated on a linear polyfit, there may be a power-law relation in how the influence propagates in this narrative over time (e.g. most of the time non-influential words, occasionally words with a high influence).
Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).
For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.
Using the alpha exponent of the fit (which is closely related to Hurst exponent)), we can better understand the nature of this relation: uniform (pulsating | alpha <= 0.65), variable (stationary, has long-term correlations | 0.65 < alpha <= 0.85), fractal (adaptive | 0.85 < alpha < 1.15), and complex (non-stationary | alpha >= 1.15).
For maximal diversity, adaptivity, and plurality, the narrative should be close to "fractal" (near-critical state). For fiction, essays, and some forms of poetry — "uniform". Informative texts will often have "variable + stationary" score. The "complex" state is an indicator that the text is always shifting its state.
Degree Distribution:
? (based on kolmogorov-smirnov test) ? switch to linear
Using this information, you can identify whether the network has scale-free / small-world (long-tail power law distribution) or random (normal, bell-shaped distribution) network properties.
This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
This may be important for understanding the level of resilience and the dynamics of propagation in this network. E.g. scale-free networks with long degree tails are more resilient against random attacks and will propagate information across the whole structure better.
If a power-law is identified, the nodes have preferential attachment (e.g. 20% of nodes tend to get 80% of connections), and the network may be scale-free, which may indicate that it's more resilient and adaptive. Absence of power law may indicate a more equalized distribution of influence.
Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.
Kolmogorov-Smirnov test compares the distribution above to the "ideal" power-law ones (^1, ^1.5, ^2) and looks for the best fit. If the value d is below the critical value cr it is a sign that the both distributions are similar.