1. Home
  2. Ad Perceptions Dataset
  3. Ad Clusters

Ad Opinion Cluster Explorer

Participants labeled ads in our dataset of 500 ads using one or more opinion labels, such as "boring", "good design", and "deceptive".

What can we say about ads using these labels? For example, what kinds of ads elicit similar reactions from people? Which opinions towards ads tend go together (e.g. "clickbait" and "deceptive")? What are the combinations of opinions that people have towards high quality ads, versus low quality ads?

To understand these questions, we clustered ads based on the distribution of opinion labels, using a technique called Population Label Distribution Learning. Below, you can explore the clusters of ads that our model produced.

Cluster Number of Ads Avg. User Rating Description
Cluster A 13 5.23 Top quality, well designed ads - mostly for consumer products
Cluster B 1
Cluster C 15 4.86 High quality, simple ads
Cluster D 115 4.53 High quality ads, mostly for consumer products
Cluster E 3 4.52 Confusing but well-designed ads
Cluster F 3
Cluster G 59 4.07 Average quality ads for consumer products
Cluster H 1
Cluster I 101 3.81 Average quality, low relevance ads (for niche interests or B2B products)
Cluster J 4 3.67 Average quality ads with political content
Cluster K 2
Cluster L 46 3.29 Average quality unattractive ads - mostly native ads or B2B ads
Cluster M 10 3.13 Ads with political content, e.g. TV programs, political T-shirts
Cluster N 3 3.12 Ads for strongly disliked products, e.g. vape pens
Cluster O 1
Cluster P 15 3.04 Poorly designed and confusing ads, i.e. unfamiliar or missing brand names
Cluster Q 29 2.95 Poorly designed clickbait ads
Cluster R 39 2.8 Clickbait ads with sexual or distasteful content
Cluster S 2 2.52
Cluster T 7 2.31 Deceptive and clickbait ads with political content
Cluster U 31 2.21 Deceptive ads, e.g. supplements and software downloads