| name | analyzing-text-sentiment |
| description | Execute this skill enables AI assistant to analyze the sentiment of text data. it identifies the emotional tone expressed in text, classifying it as positive, negative, or neutral. use this skill when a user requests sentiment analysis, opinion mining, or emoti... Use when analyzing code or data. Trigger with phrases like 'analyze', 'review', or 'examine'.
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| allowed-tools | Read, Write, Bash(cmd:*), Grep |
| version | 1.0.0 |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| license | MIT |
Sentiment Analysis Tool
This skill provides automated assistance for sentiment analysis tool tasks.
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
- Text Input: The skill receives text data as input from the user.
- Sentiment Analysis: The skill processes the text using a pre-trained sentiment analysis model to determine the sentiment polarity (positive, negative, or neutral).
- 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:
- Process the provided customer reviews.
- 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:
- Analyze the provided tweet.
- 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.
- Model Selection: Use appropriate sentiment analysis models based on the type of text being analyzed (e.g., social media, customer reviews).
Integration
This skill can be integrated with other Claude Code plugins to automate workflows, such as summarizing feedback alongside sentiment scores or triggering actions based on sentiment polarity (e.g., escalating negative feedback).
Prerequisites
- Appropriate file access permissions
- Required dependencies installed
Instructions
- Invoke this skill when the trigger conditions are met
- Provide necessary context and parameters
- Review the generated output
- Apply modifications as needed
Output
The skill produces structured output relevant to the task.
Error Handling
- Invalid input: Prompts for correction
- Missing dependencies: Lists required components
- Permission errors: Suggests remediation steps
Resources
- Project documentation
- Related skills and commands
