TY - GEN
T1 - SocialVisTUM
T2 - International Conference on Recent Advances in Natural Language Processing: Deep Learning for Natural Language Processing Methods and Applications, RANLP 2021
AU - Hagerer, Gerhard
AU - Kirchhoff, Martin
AU - Danner, Hannah
AU - Pesch, Robert
AU - Ghosh, Mainak
AU - Roy, Archishman
AU - Zhao, Jiaxi
AU - Groh, Georg
N1 - Publisher Copyright:
© 2021 Incoma Ltd. All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85123586852
U2 - 10.26615/978-954-452-072-4_054
DO - 10.26615/978-954-452-072-4_054
M3 - Conference contribution
AN - SCOPUS:85123586852
T3 - International Conference Recent Advances in Natural Language Processing, RANLP
SP - 475
EP - 482
BT - International Conference Recent Advances in Natural Language Processing, RANLP 2021
A2 - Angelova, Galia
A2 - Kunilovskaya, Maria
A2 - Mitkov, Ruslan
A2 - Nikolova-Koleva, Ivelina
PB - Incoma Ltd
Y2 - 1 September 2021 through 3 September 2021
ER -