TY - GEN
T1 - Retrieving Users' Opinions on Social Media with Multimodal Aspect-Based Sentiment Analysis
AU - Anschutz, Miriam
AU - Eder, Tobias
AU - Groh, Georg
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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 '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.
AB - 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 '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.
KW - Flickr
KW - Image retrieval
KW - Opinion mining
KW - Social media analysis
KW - multimodal
UR - http://www.scopus.com/inward/record.url?scp=85151560013&partnerID=8YFLogxK
U2 - 10.1109/ICSC56153.2023.00008
DO - 10.1109/ICSC56153.2023.00008
M3 - Conference contribution
AN - SCOPUS:85151560013
T3 - Proceedings - 17th IEEE International Conference on Semantic Computing, ICSC 2023
SP - 1
EP - 8
BT - Proceedings - 17th IEEE International Conference on Semantic Computing, ICSC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Semantic Computing, ICSC 2023
Y2 - 1 February 2023 through 3 February 2023
ER -