LDAvis is an interactive web-based visualization tool for exploring topics in Latent Dirichlet Allocation (LDA) models. It allows users to examine topic-term relationships and interpret topics by showing the most relevant terms for each topic. The system uses a combination of R and D3 to create an interactive interface that helps users understand the structure and meaning of topics in a fitted LDA model. The visualization includes two main components: a global view of topics and a detailed view of terms associated with each topic. The left panel displays topics as circles, with their sizes reflecting their prevalence and positions indicating their relationships. The right panel shows a bar chart of the most relevant terms for the selected topic, with bars representing both corpus-wide and topic-specific term frequencies. Users can interactively select topics or terms to explore their relationships. A key innovation of LDAvis is its method for determining the most relevant terms for interpreting a topic. This method, called relevance, is a weighted average of the logarithm of a term's probability and its lift. A user study was conducted to determine the optimal value of a parameter (λ) that balances the importance of a term's probability and its exclusivity to a topic. The study found that λ = 0.6 provided the best balance for topic interpretation. LDAvis also allows users to adjust the value of λ to explore different rankings of terms. The system provides a compact and interactive way to examine topic-term relationships, making it easier to understand the meaning of topics in an LDA model. The visualization is designed to help users quickly and effectively interpret topics by showing the most relevant terms and their relationships to the topics.LDAvis is an interactive web-based visualization tool for exploring topics in Latent Dirichlet Allocation (LDA) models. It allows users to examine topic-term relationships and interpret topics by showing the most relevant terms for each topic. The system uses a combination of R and D3 to create an interactive interface that helps users understand the structure and meaning of topics in a fitted LDA model. The visualization includes two main components: a global view of topics and a detailed view of terms associated with each topic. The left panel displays topics as circles, with their sizes reflecting their prevalence and positions indicating their relationships. The right panel shows a bar chart of the most relevant terms for the selected topic, with bars representing both corpus-wide and topic-specific term frequencies. Users can interactively select topics or terms to explore their relationships. A key innovation of LDAvis is its method for determining the most relevant terms for interpreting a topic. This method, called relevance, is a weighted average of the logarithm of a term's probability and its lift. A user study was conducted to determine the optimal value of a parameter (λ) that balances the importance of a term's probability and its exclusivity to a topic. The study found that λ = 0.6 provided the best balance for topic interpretation. LDAvis also allows users to adjust the value of λ to explore different rankings of terms. The system provides a compact and interactive way to examine topic-term relationships, making it easier to understand the meaning of topics in an LDA model. The visualization is designed to help users quickly and effectively interpret topics by showing the most relevant terms and their relationships to the topics.