sentiment-analysis

Analyze financial news and social media sentiment with Python libraries.

Updated Apr 21, 2026

Search Tags:#market analysis #sentiment analysis #equity research #financial news #social media analysis

Ready to run

npx skills add https://github.com/jayjpatel9717/kurukshetra --skill sentiment-analysis-jayjpatel9717

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sentiment-analysis
Source: https://github.com/jayjpatel9717/kurukshetra/tree/main/squads/stocks/agents/narad/skills/sentiment-analysis
Command: npx skills add https://github.com/jayjpatel9717/kurukshetra --skill sentiment-analysis-jayjpatel9717

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, beautifulsoup4, textblob, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies the process of assessing market sentiment, enabling informed decisions through a structured analysis of news, social media, and market signals.

Core Features & Use Cases

Sentiment Scoring: Quantitatively assesses sentiment on a scale of -1.0 to +1.0.
Trend Analysis: Identifies the direction and velocity of sentiment changes.
Catalyst Identification: Categorizes significant news items and identifies potential market-moving events.
Use Case: Gain insights into investor sentiment for a specific stock by analyzing recent news articles, social media trends, and institutional research reports.

Quick Start

Analyze the sentiment of Reliance Industries (RELIANCE.NS) for the past 7 days.

Frequently Asked Questions about sentiment-analysis

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

FAQPage Schema

How do I perform sentiment analysis on financial news and social media for equity research?▼

Sentiment analysis for equity research uses Python text processing libraries to scrape financial news and social media, assigning quantitative sentiment scores to gauge market sentiment for specific stocks.

What does market sentiment scoring quantify when analyzing financial news?▼

Market sentiment scoring quantifies text polarity on a scale from -1.0 to +1.0, identifying the direction and velocity of sentiment changes to categorize significant news items and potential market-moving events.

Do I need Beautiful Soup and TextBlob to automate market sentiment analysis workflows?▼

Yes, market sentiment analysis workflows require Beautiful Soup for web scraping financial news and TextBlob for text processing and sentiment scoring to function properly.

Can I use Python libraries to identify market-moving catalysts from social media trends?▼

Python libraries enable catalyst identification by categorizing significant news items and analyzing social media trends, helping identify potential market-moving events for specific stocks.

What's the best way to analyze investor sentiment for a specific stock over a time period?▼

Analyzing investor sentiment involves scraping recent news articles, social media trends, and institutional research reports over a set period, then applying text analysis to generate sentiment scores and trend directions.