Car Design Ratings for Text Analysis

This dataset captures consumer evaluations of car design wireframes along three perceptual dimensions relevant to automotive styling research: aggressiveness, complexity, and typicality. It contains no visual material and is therefore designed exclusively for text-based analysis.

The dataset comprises 232 distinct car design wireframes, each represented through text descriptions detailing the visual form of the design. For every wireframe, consumers rated perceived aggressiveness, perceived complexity, and perceived typicality. Each dimension includes 20 independent ratings per wireframe, resulting in more than 13,000 numeric evaluations. The ratings are stored in separate CSV files, one for each dimension, and include the wireframe ID, anonymized rater ID, and the numeric score.

In addition to the numeric evaluations, a free-text description provided by participants accompanies every wireframe. These statements capture how consumers interpreted individual design elements and why they formed their perceptions. Together, the rating data and participant statements enable quantitative and qualitative analyses of design perception.

The dataset supports a wide range of applications including text-based modeling of aesthetic impressions, computational analysis of design language, semantic feature extraction, prediction of numeric perception ratings from text, and research on variability in consumer interpretations of automotive forms.