--- name: analyzing-text-sentiment-ebcdbd56fb7f description: | This skill enables Claude 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 emotion detection on any text, such as customer reviews, social media posts, or survey responses. Trigger words include "sentiment analysis", "analyze sentiment", "opinion mining", "emotion detection", and "polarity". allowed-tools: Read, Write, Bash, Grep version: 1.0.0 --- ## 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. - **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). --- Source attribution: BbgnsurfTech Original project: https://github.com/BbgnsurfTech/claude-skills-collection/blob/01d3fafeca1133f841956b8b90b939fee92d71e7/plugins/claude-code-plugins-plus/backups/skill-structure-cleanup-20251108-073936/plugins/ai-ml/sentiment-analysis-tool/skills/sentiment-analysis-tool/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 Claude Code Plugins Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: BbgnsurfTech Original project: https://github.com/BbgnsurfTech/claude-skills-collection/blob/01d3fafeca1133f841956b8b90b939fee92d71e7/plugins/claude-code-plugins-plus/backups/skill-structure-cleanup-20251108-073936/plugins/ai-ml/sentiment-analysis-tool/skills/sentiment-analysis-tool/SKILL.md Attribution basis: Source repository record Fields of work: Marketing, Sales Roles: Content Marketer, Customer Success Manager, Marketing Analyst, PR & Communications, Social Media Manager Specialties: Specialty not specified Tools: Tool not specified Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 0eb417f0ff2267e63abb90fda9f1bc1eb013f210875fff3aad76c68ca751cb65