Evaluation of text mining to contextualize subjective acoustic assessments
- Research Area:
- Acoustic Virtual Reality,
Architectural Design Categorization
- Type of Thesis:
In the research project “Person-focused Analysis of Architectural Design”, we study human perception of built environments. The first step is to understand which dimensions are of importance to assess and verbalize the perception. Such a vocabulary can be collected by applying experimental methods such as individual vocabulary profiling. But it could also origin from already existing literature and other text-based sources. A first text mining methodology has already been developed and a first database of audio-visual attributes already exists.
In this work, you will address the question whether text mining helps to understand and contextualize subjective acoustic assessments from individuals. More specifically, the task is to derive clusters from the existing data that represent perceptive dimensions. These dimensions are then to be compared to known dimensions in acoustical research. Furthermore, individually developed attributes from experiments can be assigned to the derived clusters in order to check which dimension they match. To find the clusters, both traditional statistical methods like principal component analysis and machine learning approaches will be applied.
- Python/Matlab skills
- High interest in statistics
- Accurate and reliable work attitude