LDAvis: A method for visualizing and interpreting topics

By Carson Sievert, Kenneth E. Shirley. 2014

1,221 citations recorded by the source corpus.

We present LDAvis, a web-based interactive visualization of topics estimated using Latent Dirichlet Allocation that is built using a combination of R and D3. Our visualization provides a global view of the topics (and how they differ from each other), while at the same time allowing for a deep inspection of the terms most highly associated with each individual topic. First, we propose a novel method for choosing which terms to present to a user to aid in the task of topic interpretation, in which we define the relevance of a term to a topic. Second, we present results from a user study that suggest that ranking terms purely by their probability under a topic is suboptimal for topic interpretation. Last, we describe LDAvis, our visualization system that allows users to flexibly explore topic-term relationships using relevance to better understand a fitted LDA model.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/13e44c96680b503f799726819c56da361b20bf56

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Citations in the corpus

Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology (2018, methodology): “The discussion and interpretation were based on the models ’ ϕ matrices, i.e., word-topic distributions, and also considered varying orders of the top words using Sievert and Shirley (2014) relevance metric (explained in the next section).”
Predicting In-Game Actions from Interviews of NBA Players (2019, background): “Following Sievert and Shirley (2014), 22 we print out the words that are most associated with the z m + topic according to their estimated term frequency within the selected topic.”
Topic Modeling in Management Research: Rendering New Theory from Textual Data (2019, methodology): “First, for each topic, we analyzed the MDS plot, reordering the top words according to the relevance metric in Sievert and Shirley (2014), which altered the order between extremes of common words across topics and those uniquely within.”
A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries (2018, methodology): “The relevance of word w to topic k is measured as follows (Sievert and Shirley, 2014; Bischof and Airoldi, 2012):”
Full-Text or Abstract? Examining Topic Coherence Scores Using Latent Dirichlet Allocation (2017, methodology): “This two-dimensional representation is obtained by computing the distance between topics [41] and applying multidimensional scaling [42].”
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].”
Topic Modeling Using Latent Dirichlet allocation (2021, background): “Sievert and Shirley proposed a collaborating and web-based environment for the visualization of discovered topics [124].”
Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity (2018, positive): “We used pyLDAvis (see Sievert & Shirley, 2014) to perform multidimensional scaling on the resulting topics.”
Green, circular, bio economy: A comparative analysis of sustainability avenues (2017, methodology): “(2015) source code implementation of LDAvis (Sievert and Shirley, 2014).”