RRepoGEO

REPOGEO REPORT · LITE

laugustyniak/awesome-sentiment-analysis

Default branch master · commit ea574fbe · scanned 6/8/2026, 2:42:11 PM

GitHub: 551 stars · 109 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 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 laugustyniak/awesome-sentiment-analysis, 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
  • highabout#1
    Clarify the repository's "About" description

    Why:

    CURRENT
    Repository with all what is necessary for sentiment analysis and related areas
    COPY-PASTE FIX
    A comprehensive, curated list of awesome sentiment analysis frameworks, libraries, software, academic papers, and methods.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the root of the repository with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Set the repository's homepage URL to https://github.com/laugustyniak/awesome-sentiment-analysis or a dedicated project site if one exists.

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
0 / 2
0% of queries surface laugustyniak/awesome-sentiment-analysis
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. Flair · recommended 1×
  3. spaCy · recommended 1×
  4. NLTK · recommended 1×
  5. TextBlob · recommended 1×
  • CATEGORY QUERY
    What are the best libraries for implementing sentiment analysis with modern NLP models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Flair
    3. spaCy
    4. NLTK
    5. TextBlob

    AI recommended 5 alternatives but never named laugustyniak/awesome-sentiment-analysis. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tools for multimodal or multilingual sentiment analysis using large language models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. Google Cloud AI Platform
    3. OpenAI API
    4. PyTorch
    5. TensorFlow
    6. Microsoft Azure AI Services
    7. Amazon Comprehend
    8. Amazon Rekognition

    AI recommended 8 alternatives but never named laugustyniak/awesome-sentiment-analysis. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 laugustyniak/awesome-sentiment-analysis?
    pass
    AI did not name laugustyniak/awesome-sentiment-analysis — likely talking about a different project

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

  • If a team adopts laugustyniak/awesome-sentiment-analysis in production, what risks or prerequisites should they evaluate first?
    pass
    AI named laugustyniak/awesome-sentiment-analysis 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 laugustyniak/awesome-sentiment-analysis solve, and who is the primary audience?
    pass
    AI did not name laugustyniak/awesome-sentiment-analysis — likely talking about a different project

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

Embed your GEO score

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laugustyniak/awesome-sentiment-analysis — 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