  People post their opinions and experiences on social media, yielding rich
databases of end-users' sentiments. This paper shows to what extent machine
learning can analyze and structure these databases. An automated data analysis
pipeline is deployed to provide insights into user-generated content for
researchers in other domains. First, the domain expert can select an image and
a term of interest. Then, the pipeline uses image retrieval to find all images
showing similar content and applies aspect-based sentiment analysis to outline
users' opinions about the selected term. As part of an interdisciplinary
project between architecture and computer science researchers, an empirical
study of Hamburg's Elbphilharmonie was conveyed. Therefore, we selected 300
thousand posts with the hashtag \enquote{\texttt{hamburg}} from the platform
Flickr. Image retrieval methods generated a subset of slightly more than 1.5
thousand images displaying the Elbphilharmonie. We found that these posts
mainly convey a neutral or positive sentiment towards it. With this pipeline,
we suggest a new semantic computing method that offers novel insights into
end-users opinions, e.g., for architecture domain experts.
