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

TL;DR

SocialVisTUM is an interactive visualization toolkit that displays correlated neural topic models on social media texts, aiding exploration and analysis of large opinion mining datasets with customizable features.

Contribution

It introduces a novel interactive visualization tool for correlated neural topic models on social media data, with automatic optimization for coherence and detailed exploration features.

Findings

Visualization confirms qualitative consumer research findings

Toolkit effectively displays topic correlations and distributions

Supports exploration of large social media text collections

Abstract

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.