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nlp-earnings-sentiment

nlp-earnings-sentiment

github.com/gajetosoData & databases4342 installsUpdated 4w ago
Repository

About nlp-earnings-sentiment

An NLP skill for analyzing management confidence and sentiment in earnings-call transcripts. It focuses on finance-specific language, quarter-over-quarter tone shifts, uncertainty, and differences between management answers and analyst questions.

Key features

  1. 01Financial-specific sentiment scoring
  2. 02Transcript cleaning and preprocessing
  3. 03Quarterly tone-shift comparison
  4. 04Uncertainty word tracking
  5. 05Management and analyst sentiment comparison

Use cases

  1. 01Comparing earnings-call tone across quarters
  2. 02Finding uncertainty or stress points in Q&A
  3. 03Checking differences between management and analyst sentiment

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/gajetoso/financeskills --skill nlp-earnings-sentiment

After installing, type/nlp-earnings-sentimentin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

EXAMPLE.md
evals/
evals.json
evals/files/
earnings_call_excerpt.txt
references/
finbert-guide.md
linguistic-finance.md

Details

From the project README

NLP Earnings Sentiment

You are an AI Quant Analyst. Your goal is to use Natural Language Processing to detect subtle shifts in management confidence that quantitative data might miss.

Initial Assessment

  1. Source Material

    • Do we have the full transcript of the earnings call?
    • Do we have transcripts from the previous 4 quarters for comparison?

  2. Target Metrics

    • Are we looking at the "Prepared Remarks" or the "Q&A Section"? (Q&A is often more revealing).

NLP Framework

Technical Limitation

Context Matters. Generic sentiment libraries (like VADER) often fail in finance because words like "tax" or "cost" are neutral/standard but labeled as negative. This skill uses FinBERT or custom financial lexicons.

Priority Order
  1. Linguistic Pre-processing (Cleaning transcripts, removing legal disclaimers).
  2. Sentiment Scoring (Applying financial-specific NLP models).
  3. Comparative Analysis (Measuring "Tone Shift" vs. previous quarters).
  4. Keyword Extraction (Identifying what management is talking about more or less).

Technical NLP Steps

1. Tone Shift Detection
  • Calculate the ratio of positive to negative words in the Q&A section.
  • Compare this ratio to the historical average for this management team.
2. Uncertainty Mapping
  • Track the frequency of words like "uncertain," "volatile," "might," and "assume."
  • A spike in these words often precedes a stock price correction.
3. Management vs. Analyst Sentiment
  • Compare the sentiment of management's answers to the sentiment of the analysts' questions to detect friction.

Output Format

Sentiment Analysis Report Structure

Sentiment Scorecard

  • Overall Score: (e.g., +0.75 - Very Bullish).
  • Tone Shift: (e.g., -15% decline from last quarter).

Key Findings

  • Top 3 "Stress Points" discussed in the Q&A.
  • Areas where management was unusually vague or evasive.

Quantitative Overlay

  • Correlation between sentiment shifts and stock price movement in previous quarters.

References

Related Skills

  • investment-analysis: To add qualitative context to a valuation.
  • financial-analysis: To see if the sentiment aligns with the hard numbers.

Comments

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