Crypto Market Sentiment

Overview

This skill enables aggregation of news from popular cryptocurrency RSS feeds, performs sentiment analysis on the articles, and computes a market sentiment score ranging from -1 (highly negative) to +1 (highly positive), along with evidence-based explanations.

Workflow

Follow these steps to analyze crypto market sentiment:

1. •Select RSS Feeds: Choose popular crypto RSS feeds (see references/rss_feeds.md for a curated list).
2. •Fetch News: Retrieve recent articles from the selected feeds.
3. •Analyze Sentiment: Classify each article's sentiment as positive (+1), negative (-1), or neutral (0) based on content keywords and context.
4. •Calculate Score: Compute the average sentiment score across all articles.
5. •Generate Explanation: Provide evidence from the news items supporting the score.

Sentiment Classification Guidelines

• •Positive (+1): News about adoption, launches, partnerships, ETF approvals, price rallies, regulatory wins, or technological breakthroughs.
• •Negative (-1): News about hacks, crashes, regulatory crackdowns, liquidations, delays, or criticisms.
• •Neutral (0): Factual updates, mixed outcomes, or speculative content without clear bias.

Output Format

The skill outputs:

• •Sentiment Score: Numerical value between -1 and 1.
• •Explanation: Breakdown by feed/source, key positive/negative drivers, and overall market implications.

Resources

scripts/

• •sentiment_analyzer.py: Python script to fetch RSS feeds, parse articles, and compute sentiment score. Run with python sentiment_analyzer.py to get automated results.

references/

• •rss_feeds.md: List of popular crypto RSS feeds with URLs and descriptions.
• •sentiment_examples.md: Examples of sentiment classification for common news types.