Termite: visualization techniques for assessing textual topic models
By Jason Chuang, Christopher D. Manning, Jeffrey Heer. International Working Conference on Advanced Visual Interfaces, 2012
533 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/ea5f228caaf7283edd51a0b85ed961358d766d98
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Brandon M Stewart
- Margaret E. Roberts
- Jordan L. Boyd-Graber
- Yuening Hu
- Apurva Shah
- Uttam Chauhan
- Yuan Yunhao
- Tuan M. V. Le
In Computer Vision and Pattern Recognition
- Mohan Kankanhalli
- Ashraf Abdul
- Danding Wang
- Saleema Amershi
- Jo Vermeulen
- Ashish Kapoor
- Jordan L. Boyd-Graber
- Tuan M. V. Le
Citations in the corpus
- Short Text Topic Modeling Techniques, Applications, and Performance: A Survey (2019, background): “This new ways of labeling the topics may be more reasonable by either choosing different words or displaying the chosen words differently [62], [63].”
- Probabilistic Latent Document Network Embedding (2014, methodology): “Finally, we compare the effectiveness of PLANE as a topic model for document network (V-D).”
- ConsensUs (2018, background): “Work on topic modeling in arguments provides a strategy for augmenting deliberation technologies (Chuang et al. 2012; Klein 2012; Kriplean et al. 2012a, 2012b).”
- Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda (2018, methodology): “visualization techniques for text analysis [34, 35], tools to support interactive data analysis [24, 25, 37] and interactive machine learning [82].”
- Applications of Topic Models (2017, background): “Showing the relationships between multiple models can also help distinguish stable from spurious topics [Chuang et al., 2015], and adjusting the “hyperparameters” of distributions (the Dirichlet parameters of models discussed in Chapter 1) can have a large effect of what the final models are [Wallac”
- LDAvis: A method for visualizing and interpreting topics (2014, methodology): “A pair of overlaid bars represent both the corpus-wide frequency of a given term as well as the topic-specific frequency of the term, as in (Chuang et al., 2012b).”
- Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey (2017, methodology): “n of text in large sets of documents such as machine learning, but it is an open challenge in text mining and visualizing data in big data source. Some of the few studies that have been done, such as [201, 202, 203, 204, 205]. Chuang and et al. in [205] proposed a topic tool based on a novel visuali”
- Topic Modeling Using Latent Dirichlet allocation (2021, background): “Termite, a result-oriented visual analysis tool, suggested for judging the quality of the topic model [22].”
- A Model of Text for Experimentation in the Social Sciences (2016, background): “…are an important part of the process, we also believe that recent work in the automated visualization of topic models (Chaney and Blei 2012; Chuang et al. 2012b; Chuang,Manning, andHeer 2012a) is of equal or greater importance for helping users to substantively engage with the underling…”
- FeatureInsight: Visual support for error-driven feature ideation in text classification (2015, methodology): “For text data, possible visual summaries range from complex techniques, as in visualizations of topic models [31], to simpler techniques like " word clouds " [32], [33].”