RRepoGEO

REPOGEO REPORT · LITE

openai/generating-reviews-discovering-sentiment

Default branch master · commit 0032e4b8 · scanned 5/27/2026, 6:37:50 AM

GitHub: 1,522 stars · 377 forks

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 openai/generating-reviews-discovering-sentiment, 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 README's introductory sentence to clarify project type

    Why:

    CURRENT
    Code for Learning to Generate Reviews and Discovering Sentiment (Alec Radford, Rafal Jozefowicz, Ilya Sutskever).
    COPY-PASTE FIX
    This repository contains the original research code for "Learning to Generate Reviews and Discovering Sentiment" (Alec Radford, Rafal Jozefowicz, Ilya Sutskever), focusing on using multiplicative LSTMs for text generation and sentiment feature extraction.
  • mediumtopics#2
    Expand repository topics for better categorization

    Why:

    CURRENT
    paper
    COPY-PASTE FIX
    paper, sentiment-analysis, text-generation, language-models, research-code, multiplicative-lstm, deep-learning, nlp
  • lowreadme#3
    Add a sentence clarifying the project's historical context

    Why:

    COPY-PASTE FIX
    This project represents an early exploration into generative language models for sentiment tasks, predating and differing from modern large language models (LLMs) based on transformer architectures.

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 openai/generating-reviews-discovering-sentiment
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
VADER
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. VADER · recommended 1×
  2. TextBlob · recommended 1×
  3. spaCy · recommended 1×
  4. Hugging Face Transformers library · recommended 1×
  5. NLTK · recommended 1×
  • CATEGORY QUERY
    How to extract sentiment features from text data for classification tasks?
    you: not recommended
    AI recommended (in order):
    1. VADER
    2. TextBlob
    3. spaCy
    4. Hugging Face Transformers library
    5. NLTK
    6. Google Cloud Natural Language API
    7. Amazon Comprehend

    AI recommended 7 alternatives but never named openai/generating-reviews-discovering-sentiment. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for pre-trained language models to generate realistic product reviews or analyze existing ones.
    you: not recommended
    AI recommended (in order):
    1. GPT-3.5 / GPT-4
    2. Claude
    3. Llama 2
    4. PaLM 2 / Gemini
    5. Mistral 7B / Mixtral 8x7B
    6. Falcon
    7. BERT

    AI recommended 7 alternatives but never named openai/generating-reviews-discovering-sentiment. This is the gap to close.

    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 openai/generating-reviews-discovering-sentiment?
    pass
    AI did not name openai/generating-reviews-discovering-sentiment — 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 openai/generating-reviews-discovering-sentiment in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/generating-reviews-discovering-sentiment 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 openai/generating-reviews-discovering-sentiment solve, and who is the primary audience?
    pass
    AI did not name openai/generating-reviews-discovering-sentiment — 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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