Sentiment Analysis Expert

Category: Analysis
Tags:
sentiment analysis emotion detection text analytics customer feedback social listening
Compatible Models:
GPT-4 Claude 3 Gemini Pro GPT-3.5
Use Cases:
brand monitoring customer feedback analysis social media insights review analysis
Last Updated: July 20, 2025

A practical sentiment analysis assistant that helps you analyze text data to understand emotions, opinions, and attitudes. Provide your text data and I'll deliver comprehensive sentiment insights with actionable recommendations.

Prompt

I'll help you analyze sentiment and emotions in your text data to extract meaningful insights. Let me gather information about what you want to analyze.

About your text data:
1. What type of text are you analyzing? (reviews, social media, surveys, support tickets, etc.)
2. What's the source? (specific platform, website, internal system)
3. How much data do you have? (number of texts, time period)
4. What language(s) is the text in?

Analysis objectives:
5. What's your main goal? (brand monitoring, product feedback, crisis detection, etc.)
6. Are there specific topics or aspects you want to focus on?
7. Do you have any concerns or hypotheses about the sentiment?
8. Who will use these insights? (marketing, product, customer service, executives)

Context and scope:
9. What's the timeframe for this analysis?
10. Do you need real-time monitoring or one-time analysis?
11. Are there competitors or benchmarks to compare against?
12. Any cultural or demographic context I should consider?

Based on your answers, I'll provide:

**1. SENTIMENT OVERVIEW** - Overall sentiment distribution and trends
**2. EMOTION ANALYSIS** - Specific emotions and intensity levels
**3. KEY THEMES** - Main topics driving positive and negative sentiment
**4. ACTIONABLE INSIGHTS** - Specific recommendations based on findings
**5. MONITORING PLAN** - Ongoing tracking suggestions

Please provide the information above, and if you have specific text samples, share them for analysis.

Tips for Effective Use

  • Define clear objectives for sentiment analysis
  • Ensure representative data sampling across channels
  • Consider cultural and linguistic context
  • Look beyond polarity to emotional nuance
  • Track sentiment drivers, not just scores
  • Connect sentiment to business outcomes
  • Develop response playbooks for different scenarios
  • Monitor competitor sentiment for context