Agent skill · data analytics · jeremylongshore
analyzing-text-sentiment
Execute this skill enables AI assistant to analyze the sentiment of
Why this skill is useful
Adds a specialized sentiment analysis model that the AI wouldn't reliably generate on its own.
What it needs
Requires bash, grep installed locally. About 2k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Sentiment Analysis Tool Classify text sentiment as positive, negative, or neutral with confidence scores for customer reviews, social media posts, and survey responses. Overview This skill empowers Claude to perform sentiment analysis on text, providing insights into the emotional content and polarity of the provided data. By leveraging AI/ML techniques, it helps understand public opinion, customer feedback, and overall emotional tone in written communication. How It Works 1. Text Input: The skill receives text data as input from the user. 2. Sentiment Analysis: The skill processes the text using a pre-trained sentiment analysis model to determine the sentiment polarity (positive, negative, or neutral). 3. Result Output: The skill provides a sentiment score and classification, indicating the overall sentiment expressed in the text. When to Use This Skill This skill activates when you need to: Determine the overall sentiment of customer reviews. Analyze the emotional tone of social media posts. Gauge public opinion on a particular topic. Identify positive and negative feedback in survey responses. Examples Example 1: Analyzing Customer Reviews User request: "Analyze the sentiment of these customer reviews: 'The product is amazing!', 'The service was terrible.', 'It was okay.'" The skill will: 1. Process the provided customer reviews. 2. Classify each review as positive, negative, or neutral and provide sentiment scores. Example 2: Monitoring Social Media Sentiment User request: "Perform sentiment analysis on the following tweet: 'I love this new feature!'" The skill will: 1. Analyze the provided tweet. 2. Identify the sentiment as positive and provide a corresponding sentiment score. Best Practices Data Quality: Ensure the input text is clear and free from ambiguous language for accurate sentiment analysis. Context Awareness: Consider the context of the text when interpreting sentiment scores, as sarcasm or irony can affect results. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/analyzing-text-sentiment--5ee27b