sentiment-analysis

Name: sentiment-analysis
Availability: InStock
Author: OpenSucker

Calculate market sentiment indices from financial and social indicators.

15|2|Updated May 1, 2026

Search Tags:#social media #market analysis #sentiment #leverage #fear greed index #option ratio

Ready to run

npx skills add https://github.com/OpenSucker/OpenSucker --skill sentiment-analysis-opensucker

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sentiment-analysis
Source: https://github.com/OpenSucker/OpenSucker/tree/main/skills/vibe_skills/sentiment-analysis
Command: npx skills add https://github.com/OpenSucker/OpenSucker --skill sentiment-analysis-opensucker

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, nltk, and includes scripts (resource) and references (resource) components.

What problem does it solve?

It provides quantitative insights into market sentiment, translating subjective feelings like greed and fear into measurable indices that guide investment strategies.

Core Features & Use Cases

Market Emotion Indicators: Calculates indices such as the fear & greed index, put-call ratios, and leverage signals to assess overall sentiment.
Social Media Sentiment: Analyzes discussions and trends on platforms like Twitter, Reddit, and Weibo for real-time emotional gauges.
Use Case: An investor wants to determine if the market is overly bullish or fearful before entering a position. By examining these indicators, they can decide whether to expand or reduce holdings.

Quick Start

Use the sentiment analysis skill to view current market emotional indices and decide on appropriate trading adjustments.

Frequently Asked Questions about sentiment-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I calculate market sentiment indicators like the fear and greed index for trading?▼

Market sentiment analysis translates subjective feelings like greed and fear into measurable indices using put-call ratios and social media trends. It applies across global stock, crypto, and options markets to evaluate emotional extremes and guide trading strategies.

Can I use Python and pandas to analyze social media sentiment for crypto markets?▼

Yes, you can use Python libraries like pandas and numpy to process social media discussions from platforms like Twitter and Reddit. This helps calculate real-time emotional gauges for crypto and global stock markets to inform smarter trading decisions.

What is the best way to quantify market emotions before entering a stock position?▼

The best way to quantify market emotions before entering a stock position is by analyzing leverage signals, option ratios, and social media sentiment. These indicators help determine if the market is overly bullish or fearful, enabling you to adjust your holdings accordingly.

Do I need nltk installed to process social media discussions for market trend prediction?▼

Yes, nltk is required to process social media discussions for market trend prediction. It works alongside pandas and numpy to calculate sentiment and evaluate emotional extremes across global stock, crypto, and options markets.

How does put-call ratio analysis help with risk management in options trading?▼

Put-call ratio analysis helps with risk management by quantifying market sentiment and identifying potential emotional extremes or reversals. By evaluating these ratios, you can decide whether to expand or reduce your options holdings before entering a position.

What are the limitations of using social media sentiment for financial market analysis?▼

A limitation of using social media sentiment for financial market analysis is that emotional extremes may not always signal immediate reversals. You should combine these sentiment indicators with quantitative financial data to avoid misinterpreting market trends.