What types of text can this skill analyze?

This skill can analyze any text-based data, including direct user input, customer reviews, social media posts, survey responses, and local text files.

Does it provide a numerical score or just a label?

The skill provides both a qualitative classification (Positive/Negative/Neutral) and a quantitative sentiment score to indicate the strength of the emotion.

Can this skill handle multiple reviews at once?

Yes, it is designed to process batches of text data, classifying each entry and providing an overall sentiment profile for the dataset.

How do I trigger sentiment analysis in Claude Code?

You can trigger the skill using natural language commands like 'analyze sentiment', 'perform opinion mining', or 'check the emotional tone of this text'.

Text Sentiment Analysis

Name: Text Sentiment Analysis
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Analyzes and classifies the emotional tone of text data into positive, negative, or neutral categories for rapid opinion mining.

About

This skill empowers Claude to perform sophisticated sentiment analysis on various text formats, including customer reviews, social media feeds, and survey responses. By leveraging AI and ML patterns, it provides quantitative sentiment scores and qualitative classifications to help users gauge public opinion, monitor brand reputation, and identify actionable insights from unstructured feedback directly within their development environment.

Key Features

Classifies text into positive, negative, or neutral categories
Processes diverse inputs from customer reviews to social media posts
Automates feedback categorization using local file system tools
Identifies opinion trends across large datasets of unstructured text
Generates numerical sentiment polarity and emotional tone scores
1 GitHub stars

Use Cases

Monitoring brand reputation and public sentiment on social media platforms
Automating the categorization of large volumes of customer support feedback
Analyzing survey responses to identify key areas of user satisfaction or friction

About

Key Features

Classifies text into positive, negative, or neutral categories
Processes diverse inputs from customer reviews to social media posts
Automates feedback categorization using local file system tools
Identifies opinion trends across large datasets of unstructured text
Generates numerical sentiment polarity and emotional tone scores
1 GitHub stars

Use Cases

Monitoring brand reputation and public sentiment on social media platforms
Automating the categorization of large volumes of customer support feedback
Analyzing survey responses to identify key areas of user satisfaction or friction