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 contents 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 on 300 thousand posts from the
platform Flickr with the hashtag 'hamburg'. 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 big data analysis
method that offers new insights into end-users opinions, e.g., for architecture
domain experts.Comment: 9 pages, 5 figures, short paper version to be published at 9th
IEEE/ACM International Conference on Big Data Computing, Applications and
Technologies (BDCAT2022