RRepoGEO

REPOGEO REPORT · LITE

MaartenGr/BERTopic

Default branch master · commit f9697602 · scanned 6/27/2026, 3:32:01 AM

GitHub: 7,711 stars · 907 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
92 /100
Healthy
Category recall
2 / 2
Avg rank #1.0 when recommended
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface MaartenGr/BERTopic, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Enhance README H1 to include core value proposition

    Why:

    CURRENT
    # BERTopic
    COPY-PASTE FIX
    # BERTopic: Easily Interpretable Topic Modeling with 🤗 Transformers and c-TF-IDF
  • mediumreadme#2
    Add an explicit section detailing BERTopic's core differentiators

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## Why BERTopic?' or '## Key Differentiators', that highlights its modular, pipeline-based approach, leveraging contextual embeddings, UMAP, HDBSCAN, and c-TF-IDF for semantic understanding and interpretable topics.
  • lowtopics#3
    Add core technology components to repository topics

    Why:

    CURRENT
    bert, ldavis, machine-learning, nlp, sentence-embeddings, topic, topic-modeling, topic-modelling, topic-models, transformers
    COPY-PASTE FIX
    bert, ldavis, machine-learning, nlp, sentence-embeddings, topic, topic-modeling, topic-modelling, topic-models, transformers, umap, hdbscan, clustering

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
2 / 2
100% of queries surface MaartenGr/BERTopic
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
10%
Of all named tools, what % are you?
Top rival
Gensim
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Gensim · recommended 2×
  2. Top2Vec · recommended 2×
  3. scikit-learn · recommended 2×
  4. Mallet · recommended 1×
  5. Contextualized Topic Models · recommended 1×
  • CATEGORY QUERY
    How can I extract meaningful and interpretable topics from a large corpus of text documents?
    you: #1
    AI recommended (in order):
    1. BERTopic ← you
    2. Gensim
    3. Top2Vec
    4. scikit-learn
    5. Mallet
    6. Contextualized Topic Models
    Show full AI answer
  • CATEGORY QUERY
    What are the best Python libraries for advanced topic modeling using transformer-based embeddings?
    you: #1
    AI recommended (in order):
    1. BERTopic ← you
    2. sentence-transformers
    3. UMAP
    4. HDBSCAN
    5. Top2Vec
    6. Contextual Topic Models (CTM)
    7. ZeroShotTM
    8. CombinedTM
    9. Gensim
    10. KMeans
    11. scikit-learn
    12. TSNE
    13. PCA
    14. DBSCAN
    15. AgglomerativeClustering
    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of MaartenGr/BERTopic?
    pass
    AI named MaartenGr/BERTopic explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts MaartenGr/BERTopic in production, what risks or prerequisites should they evaluate first?
    pass
    AI named MaartenGr/BERTopic explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo MaartenGr/BERTopic solve, and who is the primary audience?
    pass
    AI named MaartenGr/BERTopic explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of MaartenGr/BERTopic. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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MaartenGr/BERTopic — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite