Retrieving Users' Opinions on Social Media with Multimodal Aspect-Based Sentiment Analysis
This provides a tool for architecture domain experts to analyze public opinions on buildings, but it is incremental as it combines existing image retrieval and sentiment analysis methods.
The paper tackled analyzing user opinions on social media by developing a multimodal aspect-based sentiment analysis pipeline, applied to 300,000 Flickr posts about Hamburg, retrieving 1,500 images of the Elbphilharmonie and finding mainly neutral or positive sentiments.
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.