Analyze finance text sentiment using FinBERT or LLM. Use when the user needs to determine the sentiment (positive/negative/neutral) and score of financial text markets.

AlphaEar Sentiment Skill Overview This skill provides sentiment analysis capabilities tailored for financial texts, supporting both FinBERT (local model) and LLM based analysis modes. Capabilities Capabilities 1. Analyze Sentiment (FinBERT / Local) Use scripts/sentiment tools.py for high speed, local sentiment analysis using FinBERT. Key Methods: analyze sentiment(text) : Get sentiment score and label using localized FinBERT model. Returns : {'score': float, 'label': str, 'reason': str} . Score Range : 1.0 (Negative) to 1.0 (Positive). batch update news sentiment(source, limit) : Batch process unanalyzed news in the database (FinBERT only). 2. Analyze Sentiment (LLM / Agentic) For higher accuracy or reasoning capabilities, YOU (the Agent) should perform the analysis using the Prompt below, calling the LLM directly, and then update the database if necessary. Sentiment Analysis Prompt Use this prompt to analyze financial texts if the local tool is insufficient or if reasoning is required. Scoring Guide: Positive (0.1 to 1.0) : Optimistic news, profit growth, policy support, etc. Negative ( 1.0 to 0.1) : Losses, sanctions, price drops, pessimism. Neutral ( 0.1 to 0.1) : Factual reporting, sideways movement, ambiguous impact. Helper Methods update single news sentiment(id, score, reason) : Use this to save your manual analysis to the database. Dependencies torch (for FinBERT) transformers (for FinBERT) sqlite3 (built in) Ensure DatabaseManager is initialized correctly.

alphaear-sentiment