Semantic Analysis: Polarization Using the Example of Age Discrimination
- Editorial Team
- 2 days ago
- 3 min read
Social networks like LinkedIn thrive not only on the opportunity to make contact, but also on communication and thus on feedback channels, such as opinions. Often not designed for this, the comments section on LinkedIn is confusing and difficult to decipher. When a post triggers hundreds of comments, how do you separate the noise from the content-rich posts? And even more importantly: How do you recognize the breadth of opinions, the pros and cons, which sometimes contradict your own?
This is where measuring polarization based on Semantic Analysis comes into play.
Semantic Analysis - Polarization on the Issue of Age Discrimination
Working people over 50: Too old? Too expensive? Misjudged?
In our real-life example analysis, we look at a specific case: A viral LinkedIn post by Jürgen Schmitt , which addresses age discrimination in the workplace. His message and initial thesis: " I'm not too old. I'm not too expensive. I'm experienced ." The post struck a nerve, and triggered a wave of reactions, which we examined in more detail.
Using our polarization matrix, a two-dimensional analysis tool, we examined what was said and how it was said, the result: a differentiated, nuanced picture of a diverse and constructive discussion.
Semantic Analysis - The Polarization Matrix
Measuring the opinion clusters regarding their agreement or disagreement with the theses in the actual LinkedIn post.
Our analysis model is based on two axes:
X-axis: Agreement (from -2 = strong disagreement to +2 = strong agreement)
Y-axis: Constructiveness (from -2 = destructive/mocking to +2 = solution-oriented)
In the case of Jürgen's post, most of the comments landed in the upper right quadrant: affirmative and constructive. This is remarkable, it shows not just agreement, but active, positive participation.
Polarization metrics: Opinion clusters form the basis
Our Semantic Analysis identified seven clear opinion clusters:
Shared experiences with age discrimination
Advocating for cross-generational teams
Criticism of the youth craze in companies
Humorous and reflective contributions
Rejection of the “too expensive” argument
Demands for structural change
Constructive counter-positions
Some clusters shared the same position in the matrix, not an error, but a hint: Different voices can converge in tone and content. For better readability, a legend helped us clearly illustrate these overlaps.
Polarization: Criticism does not automatically mean rejection
The cluster that rejects the " too expensive " argument sounds critical, but it's meant to be supportive. They agree with Jürgen Schmidt, but they vary in expressing it. This is precisely where our semantic analysis shows its strength: It recognizes the intention behind the tone and prevents misinterpretation.
Polarization: Relevance in everyday marketing
Polarization analysis is more than just drawing point clouds. It helps:
To understand online discussions in a differentiated way
Detect toxic dynamics early
To emotionally map target groups
To develop communication strategies more specifically
When companies and organizations learn to read sentiment, they make better decisions. Our semantic analysis provides the foundation for this.
Polarization - Key findings: "Age discrimination in the workplace"
In our case study, we didn't see polarization. Instead, we saw resonance. Different tones—humorous, angry, intelligent, emotional, were not a sign of division, but of depth.
Our semantic analysis has given form to this depth.
She made the flood of comments readable and highlighted the human element in them.
And that is precisely where their power lies: not in turning voices into numbers, but in making them more clearly audible.
