Does it provide numerical sentiment scores?

Yes, the skill provides both a categorical classification (positive, negative, neutral) and a sentiment score to indicate the intensity of the emotion.

Can it detect sarcasm or irony?

While it uses advanced ML models, sarcasm can be challenging. Best practices suggest considering the broader context for the most accurate interpretation.

How do I trigger sentiment analysis in Claude Code?

You can trigger the skill using keywords like 'sentiment analysis', 'opinion mining', 'emotion detection', or 'analyze sentiment' in your prompt.

Is this tool suitable for brand monitoring?

Absolutely. It is an ideal tool for gauging public opinion and monitoring brand reputation across various digital communication channels.

What types of text data can this skill analyze?

The tool is designed to process any unstructured text, including customer reviews, social media posts, support tickets, and survey responses.

Sentiment Analysis Tool

Name: Sentiment Analysis Tool
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Analyzes text data to identify emotional tone and classify sentiment as positive, negative, or neutral.

About

This skill empowers Claude to perform sophisticated sentiment analysis and opinion mining on various text formats, including customer reviews, social media posts, and survey responses. By leveraging AI and ML techniques, it provides a structured breakdown of emotional polarity and sentiment scores, helping developers and businesses gain actionable insights from qualitative data. It is particularly useful for automating feedback loops, monitoring brand reputation, and understanding public opinion at scale within the Claude Code environment.

Key Features

Identifies emotional tone and polarity across multiple data sources
Integrates with CLI workflows for automated feedback processing
Classifies text into positive, negative, or neutral categories
Supports opinion mining from social media and customer feedback
Generates sentiment scores for granular emotional quantification
1 GitHub stars

Use Cases

Extracting key insights and emotional trends from qualitative survey responses
Processing and scoring large batches of customer product reviews
Monitoring social media sentiment regarding new product or feature launches

About

Key Features

Identifies emotional tone and polarity across multiple data sources
Integrates with CLI workflows for automated feedback processing
Classifies text into positive, negative, or neutral categories
Supports opinion mining from social media and customer feedback
Generates sentiment scores for granular emotional quantification
1 GitHub stars

Use Cases

Extracting key insights and emotional trends from qualitative survey responses
Processing and scoring large batches of customer product reviews
Monitoring social media sentiment regarding new product or feature launches