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.Comment: Demo paper accepted for publication on RANLP 2021; 8 pages, 5
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