Experimental Evaluation of Dynamic Topic Modeling Algorithms

2025/08/01 by Ngozichukwuka Onah, Nadine Steinmetz, Onah, Ngozichukwuka +5
Computer Science · Social Sciences · #Advanced Text Analysis Techniques #Computational and Text Analysis Methods #Expert finding and Q&A systems #FOS: Computer and information sciences #Information Retrieval (cs.IR)

paper · pdf · doi:10.48550/arxiv.2508.00710

openalex publication_date 2025/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The amount of text generated daily on social media is gigantic and analyzing this text is useful for many purposes. To understand what lies beneath a huge amount of text, we need dependable and effective computing techniques from self-powered topic models. Nevertheless, there are currently relatively few thorough quantitative comparisons between these models. In this study, we compare these models and propose an assessment metric that documents how the topics change in time.

Citations

• Evaluating Dynamic Topic Models
• BERTopic: Neural topic modeling with a class-based TF-IDF procedure
• Top2Vec: Distributed Representations of Topics
• The COVID-19 vaccine development landscape
• The positive impact of lockdown in Wuhan on containing the COVID-19 outbreak in China
• Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
• The Dynamic Embedded Topic Model
• Multilingual Universal Sentence Encoder for Semantic Retrieval
• How Much Does Tokenization Affect Neural Machine Translation?
• Universal Sentence Encoder
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• Scaling up Dynamic Topic Models

Related

• Evaluating Dynamic Topic Models
• An Iterative Approach to Topic Modelling
• AOBTM: Adaptive Online Biterm Topic Modeling for Version Sensitive Short-texts Analysis
• Optimized Tracking of Topic Evolution
• Topic Modeling based on Keywords and Context