Applying deep learning approaches to mixed quantitative-qualitative analyses

We here verify whether a quantitative approach, i.e., a deep learning-based one, may be used to synthesize a model apt to perform specific qualitative analyses. To this aim, we leverage a previous contribution, where we approached the concrete problem of implementing a socio-historical classification toolchain for a collection of vernacular photos. In such a work, after individuating a corpus of vernacular photographs we devised the process that follows. First, we resorted to existing socio-historical categories derived from previous qualitative studies. Secondly, we involved the people included in the photos in the annotation process of a subset of the corpus of data. We then fine-tuned and deployed existing deep learning models to classify the entire corpus of data. Finally, we compared the results obtained with our approach to the ones obtained by a socio-historian. We hence here focus on the relationship between quantitative and qualitative methods considering the specific case of socio-historical analyses.