BERTopic: Benefits and Limitations

Understanding when to use BERTopic versus other topic modeling approaches isn’t always straightforward—this comprehensive Google Sheets comparison breaks down BERTopic’s strengths and weaknesses across five critical dimensions. Created by Lazarina Stoy for her Introduction to ML for SEOs course, this reference guide helps you make informed decisions about whether BERTopic is the right choice for your specific content analysis needs by honestly examining its trade-offs against traditional methods like LDA/NMF and other embedding-based approaches like Top2Vec.
The guide evaluates BERTopic through five key lenses: Performance (efficiency and accuracy), Interpretability, Scalability, comparison with traditional topic models, and comparison with other embedding-based methods. Each section presents both benefits and limitations side-by-side with specific technical details and practical implications. You’ll learn that while BERTopic excels at semantic coherence and capturing contextual meaning, it’s computationally intensive and may struggle with very small datasets. The guide explains technical nuances like BERTopic’s stochastic clustering output, its single-topic-per-document limitation, and its memory requirements for large corpora—helping you anticipate challenges before implementation. Links to source documentation (pmc.ncbi.nlm.nih.gov) throughout provide deeper technical reading for those who want to understand the research behind each point.
Use this for:
‧ Deciding whether BERTopic’s computational costs justify its semantic advantages for your specific dataset size and content type
‧ Understanding when traditional methods like LDA might actually be more appropriate despite being older technology
‧ Anticipating technical challenges around scalability, memory usage, and preprocessing requirements before starting implementation
‧ Comparing BERTopic with Top2Vec and other embedding-based alternatives to choose the best fit for your use case
‧ Setting realistic expectations about topic quality, granularity, and interpretability based on your corpus characteristics
This is perfect for SEO professionals and data scientists evaluating topic modeling options who need an honest, practical assessment of BERTopic’s capabilities and constraints to avoid investing time in the wrong approach for their specific content analysis challenge.