DTECT: Dynamic Topic Explorer & Context Tracker

TL;DR

DTECT is an integrated system that enhances the analysis and interpretation of evolving topics in large textual datasets through visualization, automatic labeling, and user-friendly interfaces.

Contribution

It introduces a comprehensive platform combining multiple models, interpretability tools, and interactive features for dynamic topic analysis, addressing fragmentation in existing methods.

Findings

Improves interpretability with LLM-driven automatic topic labeling.

Enables effective trend analysis with temporally salient words.

Provides an interactive visualization and summarization interface.

Abstract

The explosive growth of textual data over time presents a significant challenge in uncovering evolving themes and trends. Existing dynamic topic modeling techniques, while powerful, often exist in fragmented pipelines that lack robust support for interpretation and user-friendly exploration. We introduce DTECT (Dynamic Topic Explorer & Context Tracker), an end-to-end system that bridges the gap between raw textual data and meaningful temporal insights. DTECT provides a unified workflow that supports data preprocessing, multiple model architectures, and dedicated evaluation metrics to analyze the topic quality of temporal topic models. It significantly enhances interpretability by introducing LLM-driven automatic topic labeling, trend analysis via temporally salient words, interactive visualizations with document-level summarization, and a natural language chat interface for intuitive data querying. By integrating these features into a single, cohesive platform, DTECT empowers users to more effectively track and understand thematic dynamics. DTECT is open-source and available at https://github.com/AdhyaSuman/DTECT.