Topic Modeling (TM) is a widely used method for discovering hidden themes in unlabeled text collections, but the resulting topics are often difficult to interpret. Most existing visualizations present topics separately and do not show the words that link them, offering only a partial view of how topics relate to one another. This work introduces a graph‑based representation in which topics and words are modeled as connected nodes. This structure makes shared words explicit and allows users to observe how individual terms contribute to multiple thematic areas within the corpus. By encoding topic–word relationships as edges, the visualization reveals the distribution of these connections and provides a more detailed perspective than traditional topic‑centric displays. The graph includes four node types, defined by their role and degree (number of connections). The developed tool, topicwordgraph, offers two complementary visualizations: a main graph representation that distinguishes node types by color, and two bar charts that summarize topic overlap and word memberships. Together, these views help users understand how topics intertwine through shared vocabulary. By making the shared words between topics explicit, this tool offers a practical and effective way to interpret how topics are interconnected.

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• Computer and Information Sciences - Natural Sciences