topicwizard -- a Modern, Model-agnostic Framework for Topic Model Visualization and Interpretation
This addresses the challenge for users in interpreting complex topic models across various applications like discourse analysis and text filtering, though it is incremental as it builds on existing visualization utilities.
The authors tackled the problem of limited and biased interpretation of topic models by introducing topicwizard, a model-agnostic framework that provides intuitive and interactive visualization tools, resulting in a more complete and accurate understanding of topic model outputs.
Topic models are statistical tools that allow their users to gain qualitative and quantitative insights into the contents of textual corpora without the need for close reading. They can be applied in a wide range of settings from discourse analysis, through pretraining data curation, to text filtering. Topic models are typically parameter-rich, complex models, and interpreting these parameters can be challenging for their users. It is typical practice for users to interpret topics based on the top 10 highest ranking terms on a given topic. This list-of-words approach, however, gives users a limited and biased picture of the content of topics. Thoughtful user interface design and visualizations can help users gain a more complete and accurate understanding of topic models' output. While some visualization utilities do exist for topic models, these are typically limited to a certain type of topic model. We introduce topicwizard, a framework for model-agnostic topic model interpretation, that provides intuitive and interactive tools that help users examine the complex semantic relations between documents, words and topics learned by topic models.