RRepoGEO

REPOGEO REPORT · LITE

xiamx/awesome-sentiment-analysis

Default branch master · commit 11ef0286 · scanned 6/15/2026, 1:52:57 PM

GitHub: 931 stars · 164 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 xiamx/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
  • highreadme#1
    Reposition the README's opening to clarify it's an awesome list, not a library

    Why:

    CURRENT
    Curated list of Sentiment Analysis methods, implementations and misc.
    COPY-PASTE FIX
    This is an **awesome list** – a curated collection of Sentiment Analysis methods, implementations, and resources. It serves as a comprehensive guide for researchers and developers, not a deployable library or tool.
  • mediumabout#2
    Refine the repository description for clarity and keyword density

    Why:

    CURRENT
    😀😄😂😭 A curated list of Sentiment Analysis methods, implementations and misc. 😥😟😱😤
    COPY-PASTE FIX
    A curated awesome list of Sentiment Analysis methods, implementations, and resources for researchers and developers.
  • lowhomepage#3
    Add the repository URL as the homepage in the About section

    Why:

    COPY-PASTE FIX
    https://github.com/xiamx/awesome-sentiment-analysis

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 xiamx/awesome-sentiment-analysis
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. nltk/nltk · recommended 1×
  3. explosion/spaCy · recommended 1×
  4. sloria/TextBlob · recommended 1×
  5. flairNLP/flair · recommended 1×
  • CATEGORY QUERY
    What open-source libraries are available for integrating sentiment analysis into my application?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. NLTK (nltk/nltk)
    3. spaCy (explosion/spaCy)
    4. TextBlob (sloria/TextBlob)
    5. Flair (flairNLP/flair)
    6. VADER (cjhutto/vaderSentiment)

    AI recommended 6 alternatives but never named xiamx/awesome-sentiment-analysis. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive list of sentiment analysis techniques and tools?
    you: not recommended
    AI recommended (in order):
    1. Awesome Sentiment Analysis
    2. Papers With Code
    3. Kaggle
    4. MonkeyLearn
    5. Towards Data Science
    6. NLTK

    AI recommended 6 alternatives but never named xiamx/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 xiamx/awesome-sentiment-analysis?
    pass
    AI did not name xiamx/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 xiamx/awesome-sentiment-analysis in production, what risks or prerequisites should they evaluate first?
    pass
    AI named xiamx/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 xiamx/awesome-sentiment-analysis solve, and who is the primary audience?
    pass
    AI did not name xiamx/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?

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xiamx/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