The paper introduces an approach to topic model visualization that is characterized by wide possibilities of choosing a method of visualization, user-friendly model representation, and simplicity of implementation for applications. The existing approaches to topic models visualization have been analyzed, and a system, which allows choosing data source for topic models, changing modeling parameters and visualizing the result of topic modeling with IPython has been developed. The example of topic model visualization has been built using the SCTM-en corpus of original news text
Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but re...
Text visualization and visual analytics are important to find knowledge buried inside the piles of t...
Topic modeling is a method of statistically identifying abstract topics that are present throughout ...
In this paper, we surveyed recent publications on topic modeling and analyzed the forms of visualiza...
In this paper, we surveyed recent publications on topic modeling and analyzed the forms of visualiza...
In this paper, we surveyed recent publications on topic modeling and analyzed the forms of visualiza...
Topic models remain a black box both for modelers and for end users in many respects. From the model...
In this paper, we surveyed recent publications on topic modeling and analyzed the forms of visualiza...
We present TopicNets, a Web-based system for visual and interactive analysis of large sets of docume...
Existing algorithms for understanding large collections of documents often pro-duce output that is n...
Analysis tools based on topic models are often used as a means to explore large amounts of unstructu...
Analysis tools based on topic models are often used as a means to explore large amounts of unstructu...
Topic modeling is a machine learning technique that identifies latent topics in a text corpus. There...
Topic Modelling has been widely used in the fields of machine learning, text mining etc. It was prop...
We present LDAExplore, a tool to visualize topic distributions in a given document corpus that are g...
Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but re...
Text visualization and visual analytics are important to find knowledge buried inside the piles of t...
Topic modeling is a method of statistically identifying abstract topics that are present throughout ...
In this paper, we surveyed recent publications on topic modeling and analyzed the forms of visualiza...
In this paper, we surveyed recent publications on topic modeling and analyzed the forms of visualiza...
In this paper, we surveyed recent publications on topic modeling and analyzed the forms of visualiza...
Topic models remain a black box both for modelers and for end users in many respects. From the model...
In this paper, we surveyed recent publications on topic modeling and analyzed the forms of visualiza...
We present TopicNets, a Web-based system for visual and interactive analysis of large sets of docume...
Existing algorithms for understanding large collections of documents often pro-duce output that is n...
Analysis tools based on topic models are often used as a means to explore large amounts of unstructu...
Analysis tools based on topic models are often used as a means to explore large amounts of unstructu...
Topic modeling is a machine learning technique that identifies latent topics in a text corpus. There...
Topic Modelling has been widely used in the fields of machine learning, text mining etc. It was prop...
We present LDAExplore, a tool to visualize topic distributions in a given document corpus that are g...
Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but re...
Text visualization and visual analytics are important to find knowledge buried inside the piles of t...
Topic modeling is a method of statistically identifying abstract topics that are present throughout ...