WordForce: Visualizing Controversial Words in Debates

Wei-Fan Chen, Fang-Yu Lin, Lun-Wei Ku


Abstract
This paper presents WordForce, a system powered by the state of the art neural network model to visualize the learned user-dependent word embeddings from each post according to the post content and its engaged users. It generates the scatter plots to show the force of a word, i.e., whether the semantics of word embeddings from posts of different stances are clearly separated from the aspect of this controversial word. In addition, WordForce provides the dispersion and the distance of word embeddings from posts of different stance groups, and proposes the most controversial words accordingly to show clues to what people argue about in a debate.
Anthology ID:
C16-2057
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
273–277
Language:
URL:
https://aclanthology.org/C16-2057
DOI:
Bibkey:
Cite (ACL):
Wei-Fan Chen, Fang-Yu Lin, and Lun-Wei Ku. 2016. WordForce: Visualizing Controversial Words in Debates. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, pages 273–277, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
WordForce: Visualizing Controversial Words in Debates (Chen et al., COLING 2016)
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PDF:
https://aclanthology.org/C16-2057.pdf