用最优传输合并主题模型，实时追踪数据流中的话题变化。
Merging Embedded Topics with Optimal Transport for Online Topic Modeling on Data Streams
- 基于嵌入式主题模型，用非平衡最优传输融合连续批次模型。
- 在模拟与真实数据上均优于现有方法，能准确捕捉话题演变。
- 适合关注社交媒体话题动态的从业者与研究者。
主题建模是无监督学习中的关键组件，用于识别文本语料库中的主题。社交媒体的迅猛发展每日产生海量文本数据，使在线主题建模方法成为管理持续流入数据流的关键。本文提出一种名为StreamETM的新方法，基于嵌入式主题模型（ETM），通过非平衡最优传输合并连续部分文档批次的学习模型来处理数据流。同时，采用在线变化点检测算法识别话题随时间的变化，从而发现文本流动力学的重要转变。在模拟数据和真实数据上的数值实验表明，StreamETM性能优于对比方法。代码已公开：https://github.com/fgranese/StreamETM。
原文摘要 · Abstract (English)
Topic modeling is a key component in unsupervised learning, employed to identify topics within a corpus of textual data. The rapid growth of social media generates an ever-growing volume of textual data daily, making online topic modeling methods essential for managing these data streams that continuously arrive over time. This paper introduces a novel approach to online topic modeling named StreamETM. This approach builds on the Embedded Topic Model (ETM) to handle data streams by merging models learned on consecutive partial document batches using unbalanced optimal transport. Additionally, an online change point detection algorithm is employed to identify shifts in topics over time, enabling the identification of significant changes in the dynamics of text streams. Numerical experiments on simulated and real-world data show StreamETM outperforming competitors. We provide the code publicly available at https://github.com/fgranese/StreamETM.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。