AI-powered classifications vs Keywords. Part 2/2: Evolution over time.

For content selection: AI-powered classifications can sense Editorial Orientations AND Evolution over time. Keywords cannot.

For years, access to knowledge was all about the presence or absence of keywords to trigger the selection of content: A 1-dimensional access, keywords based, to knowledge. Linear. Limited to 0 (absent) or 1 (present).

Last week, we covered the first advantage of AI-Powered Classifications vs keywords based selection, Editorial Orientations, and showed how the same event, on 3 different publications can have different Editorial Orientations.

This is an additional dimension to access knowledge.

Read postLet’s now have a look at a 3rd dimension: Sensitivity over time.

Perception of an event evolves with time, so do our AI-Powered classifications.

France has been through a lot of social movements with the pension reform the French government is pushing for.

From the beginning of the protests until now, the perception has evolved.

Let’s look at the same article and how AI classifies it at two different times.

This article was published on Dec 10th 2019:

Pension reform: “It would be a misdiagnosis to talk about minced runs.”  [google Translation] (Réforme des retraites: “Ne parler que de parcours hachés serait une erreur de diagnostic”)

On Dec 10th, top classification was:

We are at the beginning of the movement, Employment and Unemployment is the top classification.

On Dec 31st, top classifications are now:

3 weeks later, the very same article with the very same content is classified as Senior first, then Social Assistance and, now in 3rd, Employment and Unemployment

Clearly, after 3 weeks of protests, Aging and Social are topping the Employment dimension.

How can AI-Powered Classification do this?

In a previous post, we explained how our AI worked:

How our AI-powered classification works.

Every new article is classified as follow:

Which means the day the article is published, we use Classifications Datasets (aka bags of words) on that very day.

Classification Datasets are also updated to sync with every single classification and sense the depth of expertise over time. This means some words can be in and out and with a different weight over time. This means classifications are set, by default, for the day an article is published but can be re-run on a different day and produce a different classification. Like in real life, your perception of something evolves with time.

Why it matters.

Simply because time is a vital dimension of perception.

Simply relying on the presence of keywords to select content for analytics, expose your brand via advertising etc… is dangerous.

What’s true at publication time might not be at analytics time, or advertising time…

In the example above, you may or may not want articles about “Seniors”. At publication time, the article was under the radar, 3 weeks later it is classified as “Seniors”. Is it still where your brand wants to be exposed? are those content the one you want to analyze today? do those articles matter for the education of your teams?

Relying on keywords that are present in content forever, not only does not give you the orientation of the content but is not sensitive to the evolution of perception. And as we know, in Marketing:

Perception is reality.

Questions? Ask!

 

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Published by

Freddy Mini

CEO & Co-founder