AI-Powered Classification vs Keywords

Going beyond a 1-dimensional access to knowledge.

For years, access to knowledge has been ruled by keywords presence. Search engines, corpus selection for business intelligence, DSPs for online advertising, Brand Safety, Watch alerts... All is 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).

Keywords presence does not sense angles, subtlety and orientations taken by the author (nor the sensitivity over time. Today's meanings are the same any other day).
For example, the presence of "Christmas gift" might be "ok" but is it in a context of "Military defense" and "Weapon"? Can you maintain queries excluding all related, always evolving dictionaries of synonyms and be sure your brand won't be exposed? After all, a word can have several meanings depending on its context and the time it is read. AI-Powered Classifications are the solution:

AI-Powered Classifications are adding 2 more dimensions: Editorial orientations and evolution over time.


1. Editorial orientations detection.

Both, AI-Powered Classifications and keywords based selections are unbiased, universal and up-to-date. Because TrustedOut is AI-Powered, our machine learning guarantees the same non-humain, machine powered benefits.

Editorial Orientations Example:
1 event, 2 countries, 3 articles, 10 classifications.

The event: let's take North Korea announcing a special Gift to the US. The 2 countries: We then selected 3 articles from a Google Search on "North Korea Gift" for the US and "Coree du Nord Cadeau" for France. The 3 articles: we randomly picked USAToday, CBSNews and Le Figaro. Here are the top 10 classifications TrustedOut came up with. For each we've added how the media is spotted for its Political Orientations (beta)

USAToday



Vase or missiles? US awaits Christmas 'gift' from North Korea's Kim

1 General › Politics › Diplomacy
2 General › Politics › International
3 Industries › Aerospace And Defense › Weapon
4 General › Politics › Military Defense
5 General › Politics › Civil Defense
6 Industries › Energy › Nuclear Power
7 Industries › Aerospace And Defense › Naval System
8 General › Politics › Administration
9 Industries › Aerospace And Defense › Aerospace Systems
10 General › Politics › Government


CBSNews

No sign of "Christmas gift" from North Korea yet, but deadline looms

1 General › Politics › Military Defense
2 Industries › Aerospace And Defense › Weapon
3 General › Politics › Diplomacy
4 General › Politics › International
5 Industries › Aerospace And Defense › Naval System
6 Industries › Aerospace And Defense › Aerospace Systems
7 Industries › Aerospace And Defense › Missiles And Rockets
8 Industries › Energy › Nuclear Power
9 Industries › Aerospace And Defense › Satellite
10 Industries › Transportation › Ship


Le Figaro

Trump is hoping for a "nice vase" instead of a North Korean missile for Christmas. (Trump espère un «beau vase» au lieu d'un missile nord-coréen pour Noël)

1 General › Politics › Diplomacy
2 Industries › Aerospace And Defense › Weapon
3 Industries › Aerospace And Defense › Aerospace Systems
4 Industries › Aerospace And Defense › Missiles And Rockets
5 General › Politics › International
6 General › Politics › Military Defense
7 People › Society › Opinion And Idea
8 Industries › Aerospace And Defense › Satellite
9 General › Law › International
10 Industries › Aerospace And Defense › Aircraft


Editorial Angles

Here's a summary of the classifications for the 3 articles:

A few remarks:

Here's how TrustedOut saw the Aerospace and Defense Industry, back in October

2. Evolution over time.



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

We previously 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 post

Let'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?

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.