DTECT: Dynamic Topic Explorer & Context Tracker

2025-07-10

AI Summary

To address the challenge of efficiently modeling and interpreting dynamic topic evolution amid explosive growth of temporal text data, this paper proposes an end-to-end dynamic topic analysis system. Methodologically, it integrates dynamic topic modeling, LLM-driven automatic topic labeling, time-sensitive salient keyword trend analysis, interactive visualization, and a natural language dialogue interface—enabling a closed-loop pipeline spanning preprocessing, multi-model training, and topic quality evaluation. Its key contribution lies in the first deep integration of large language models (LLMs) into dynamic topic labeling and interpretation, synergistically leveraging temporally aware keyword extraction and document-level summarization to substantially enhance interpretability and interactivity. The system is open-source; empirical evaluation on large-scale temporal corpora demonstrates its capability to accurately capture fine-grained topic evolution and support intuitive, exploratory analysis.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Topic discovery and tracking
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Uncovering evolving themes in rapidly growing textual data
• Addressing fragmented pipelines in dynamic topic modeling
• Enhancing interpretability and user-friendly exploration of temporal insights

Innovation

• End-to-end system for temporal topic modeling
• LLM-driven automatic topic labeling
• Interactive visualizations with document summarization