# Retrieving Users' Opinions on Social Media with Multimodal Aspect-Based Sentiment Analysis

arXiv: https://arxiv.org/abs/2210.15377 · HTML: https://mlsanity.com/paper/2210.15377/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 27 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Large-Scale Image Retrieval with Attentive Deep Local Features](https://mlsanity.com/paper/1612.06321/) (2016)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [Learning with Average Precision: Training Image Retrieval with a Listwise Loss](https://mlsanity.com/paper/1906.07589/) (2019)
- [End-to-end Learning of Deep Visual Representations for Image Retrieval](https://mlsanity.com/paper/1610.07940/) (2016)
- [Sentiment Analysis Based on Deep Learning: A Comparative Study](https://mlsanity.com/paper/2006.03541/) (2020)
- [BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs](https://mlsanity.com/paper/1704.06125/) (2017)
- [Variational Semi-supervised Aspect-term Sentiment Analysis via Transformer](https://mlsanity.com/paper/1810.10437/) (2018)
- [Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification](https://mlsanity.com/paper/1705.02023/) (2017)

## Built on by (most cited first, 1 of 1)

- [Recent Advances in Multimodal Affective Computing: An NLP Perspective](https://mlsanity.com/paper/2409.07388/) (2024)

## Abstract (arXiv)

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 enquotetexttthamburg 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.