# SocialVisTUM: An Interactive Visualization Toolkit for Correlated Neural Topic Models on Social Media Opinion Mining

arXiv: https://arxiv.org/abs/2110.10575 · HTML: https://mlsanity.com/paper/2110.10575/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 20 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised](https://mlsanity.com/paper/1808.08858/) (2018)
- [Coherence-Aware Neural Topic Modeling](https://mlsanity.com/paper/1809.02687/) (2018)
- [Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training](https://mlsanity.com/paper/1909.00415/) (2019)
- [Topic Similarity Networks: Visual Analytics for Large Document Sets](https://mlsanity.com/paper/1409.7591/) (2014)
- [Text Network Exploration via Heterogeneous Web of Topics](https://mlsanity.com/paper/1610.00219/) (2016)

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

- [Efficient Topic Extraction via Graph-Based Labeling: A Lightweight Alternative to Deep Models](https://mlsanity.com/paper/2511.04248/) (2025)

## Abstract (arXiv)

Recent research in opinion mining proposed word embedding-based topic modeling methods that provide superior coherence compared to traditional topic modeling. In this paper, we demonstrate how these methods can be used to display correlated topic models on social media texts using SocialVisTUM, our proposed interactive visualization toolkit. It displays a graph with topics as nodes and their correlations as edges. Further details are displayed interactively to support the exploration of large text collections, e.g., representative words and sentences of topics, topic and sentiment distributions, hierarchical topic clustering, and customizable, predefined topic labels. The toolkit optimizes automatically on custom data for optimal coherence. We show a working instance of the toolkit on data crawled from English social media discussions about organic food consumption. The visualization confirms findings of a qualitative consumer research study. SocialVisTUM and its training procedures are accessible online.