Does it provide a numerical score or just a label?

The skill provides both a categorical classification (Positive, Negative, or Neutral) and a sentiment score to indicate the strength of the detected emotion.

Can it detect sarcasm or irony?

While the skill uses advanced ML models, sarcasm remains a challenge for NLP; it is best practice to consider the broader context when interpreting results for nuanced text.

How do I trigger this skill in Claude Code?

You can trigger the skill by using natural language commands like 'analyze sentiment', 'opinion mining', or 'detect the emotion in this text'.

What types of text can this skill analyze?

It can analyze any written text data, including social media posts, customer reviews, support tickets, survey responses, and even long-form articles.

Is this skill suitable for bulk processing?

Yes, you can provide multiple reviews or a collection of feedback items, and the skill will process and classify each entry accordingly.

Sentiment Analysis & Opinion Mining

Name: Sentiment Analysis & Opinion Mining
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Analyzes text data to identify emotional tone, classifying content as positive, negative, or neutral for actionable insights.

About

This skill equips Claude with specialized capabilities to process and interpret the emotional sentiment behind written text. By classifying inputs as positive, negative, or neutral, it allows users to quickly quantify public opinion, monitor brand reputation across social media, and evaluate customer satisfaction from reviews or surveys. It is particularly useful for developers and data scientists who need to automate the extraction of subjective information from large datasets to drive data-informed decision-making and enhance context-aware applications.

Key Features

Classifies text into positive, negative, or neutral categories
Supports opinion mining across customer reviews and social media posts
Provides numerical sentiment scores for granular polarity analysis
Detects emotional tone to improve context-aware AI interactions
3 GitHub stars
Integrates with automation workflows for feedback escalation

Use Cases

Analyzing open-ended survey responses to identify user pain points
Evaluating customer satisfaction by processing batch product reviews
Monitoring brand health through automated social media sentiment tracking

About

Key Features

Classifies text into positive, negative, or neutral categories
Supports opinion mining across customer reviews and social media posts
Provides numerical sentiment scores for granular polarity analysis
Detects emotional tone to improve context-aware AI interactions
3 GitHub stars
Integrates with automation workflows for feedback escalation

Use Cases

Analyzing open-ended survey responses to identify user pain points
Evaluating customer satisfaction by processing batch product reviews
Monitoring brand health through automated social media sentiment tracking