让动态主题模型更易用：自动标注+自然语言查询，一眼看懂文本趋势变化。
DTECT: Dynamic Topic Explorer & Context Tracker
- 用大模型自动生成主题标签，省去人工命名麻烦。
- 通过时间敏感词追踪主题演变，支持按时间线分析趋势。
- 提供可视化界面和聊天式查询，适合非技术用户探索数据。
文本数据的爆炸式增长使得识别随时间演化的主题与趋势面临挑战。现有动态主题建模方法虽强大，但常以零散流程存在，缺乏可解释性与用户友好探索支持。我们提出 DTECT（Dynamic Topic Explorer & Context Tracker），一个端到端系统，弥合原始文本与时间洞察之间的差距。DTECT 提供统一工作流，涵盖数据预处理、多种模型架构及专用评估指标，用于分析时序主题模型的主题质量。它通过大模型驱动的自动主题标注、基于时间显著词的趋势分析、文档级摘要的交互式可视化，以及自然语言聊天接口，显著提升可解释性。整合这些功能于单一平台，使用户能更高效地追踪与理解主题动态。DTECT 已开源，项目地址为 https://github.com/AdhyaSuman/DTECT。
原文摘要 · Abstract (English)
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.
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