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