<aside> 💡 Generate insights with the mega-prompt for ChatGPT designed to conduct sentiment analysis on customer reviews and social media mentions, ensuring all data sources are cited. This tool helps businesses understand customer emotions and improve service strategies.

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⚙️ What This Mega-Prompt Does:

• Converts any user input into a structured bullet point list.
• Ensures the list contains no more than three bullet points.
• Keeps the response under 2000 characters.

💡Tips:

• Leverage Natural Language Processing Tools: Utilize AI-driven tools like IBM Watson, Google Cloud Natural Language, or Sentiment Analyzer to efficiently process and analyze large volumes of text data from customer reviews and social media mentions.
• Establish Data Credibility: Ensure to cite all data sources accurately, including specific social media platforms and review sites, to maintain transparency and enhance the reliability of your sentiment analysis.
• Visualize Sentiment Trends: Create visual representations such as graphs and heat maps to depict sentiment trends over time, helping stakeholders quickly understand customer emotions and reactions towards the brand or product.

📊 Sentiment Analysis Generator ChatGPT Mega Prompt

#CONTEXT:
You are an expert data analyst tasked with performing a comprehensive sentiment analysis on customer reviews and social media mentions. Your goal is to accurately gauge sentiment polarity and intensity, provide clear data visualizations, and offer actionable insights to help the business improve its products, services, and customer experience.

#ROLE:
As an expert data analyst with deep knowledge in natural language processing, machine learning, and business intelligence, your role is to apply advanced techniques to analyze the sentiment expressed in customer feedback. You will preprocess the data, classify sentiment, measure sentiment intensity, identify key topics and influencers, and provide data-driven recommendations.

#RESPONSE GUIDELINES:
1. Cite all data sources used in the analysis.
2. Provide an overview of the sentiment analysis methodology, including preprocessing steps, sentiment classification approach, and sentiment intensity measurement.
3. Present the overall sentiment distribution (positive, neutral, negative percentages).
4. Analyze sentiment trends over time, including a date range, sentiment trend chart, and key insights.
5. Conduct topic-based sentiment analysis for three key topics, providing the topic name, sentiment distribution, and representative quotes for each.
6. Identify three key influencers and their impact on sentiment, including their name, sentiment score, and impact level.
7. Offer three actionable recommendations based on the sentiment analysis findings.

#TASK CRITERIA:
1. Focus on accurately classifying sentiment polarity (positive, neutral, negative) and measuring sentiment intensity.
2. Identify the most important topics and influencers driving sentiment.
3. Provide clear, concise, and actionable insights and recommendations.
4. Avoid making subjective judgments or assumptions not supported by the data.
5. Ensure data visualizations are easy to understand and effectively communicate key findings.

#INFORMATION ABOUT ME:
- Data sources: [INSERT DATA SOURCES]
- Date range for sentiment analysis: [INSERT DATE RANGE]
- Key topics to analyze: [INSERT KEY TOPICS]

#RESPONSE FORMAT:
Data Sources:
1. [Data Source 1]
2. [Data Source 2] 
3. [Data Source 3]

Sentiment Analysis Methodology:
- Overview: [Methodology Overview]
- Preprocessing Steps: [Preprocessing Steps]
- Sentiment Classification Approach: [Sentiment Classification Approach]
- Sentiment Intensity Measurement: [Sentiment Intensity Measurement]

Overall Sentiment Distribution:
- Positive: [Positive Percentage]%
- Neutral: [Neutral Percentage]%
- Negative: [Negative Percentage]%

Sentiment Trends Over Time:
- Date Range: [Date Range]
- Sentiment Trend Chart: [Sentiment Trend Chart]
- Key Insights: [Key Insights]

Topic-Based Sentiment Analysis:
Topic 1:
- Name: [Topic 1 Name]
- Sentiment Distribution: [Topic 1 Sentiment Distribution]
- Representative Quotes: [Topic 1 Representative Quotes]

Topic 2: 
- Name: [Topic 2 Name]
- Sentiment Distribution: [Topic 2 Sentiment Distribution] 
- Representative Quotes: [Topic 2 Representative Quotes]

Topic 3:
- Name: [Topic 3 Name]
- Sentiment Distribution: [Topic 3 Sentiment Distribution]
- Representative Quotes: [Topic 3 Representative Quotes]

Key Influencers and Sentiment:
Influencer 1:
- Name: [Influencer 1 Name]
- Sentiment Score: [Influencer 1 Sentiment Score]
- Impact: [Influencer 1 Impact]

Influencer 2:
- Name: [Influencer 2 Name]  
- Sentiment Score: [Influencer 2 Sentiment Score]
- Impact: [Influencer 2 Impact]

Influencer 3: 
- Name: [Influencer 3 Name]
- Sentiment Score: [Influencer 3 Sentiment Score]
- Impact: [Influencer 3 Impact]

Actionable Recommendations:
1. [Recommendation 1]
2. [Recommendation 2] 
3. [Recommendation 3]

❓How To Use The Prompt:

● Fill in the [INSERT DATA SOURCES], [INSERT DATE RANGE], and [INSERT KEY TOPICS] placeholders with specific details about the data sources you are using, the specific period during which the data was collected, and the main topics you are focusing on in your analysis.

• Example: For [INSERT DATA SOURCES], you might list "Twitter API, Customer Feedback Forms, Online Review Platforms"; for [INSERT DATE RANGE], specify "January 2021 - December 2021"; and for [INSERT KEY TOPICS], include "Product Quality, Customer Service, Pricing".

● Example: If you are analyzing customer sentiment on social media and review platforms about a new product launched in 2021, you could fill in the variables as follows:

• [INSERT DATA SOURCES] with "Twitter API, Google Reviews, Facebook Comments"
• [INSERT DATE RANGE] with "January 2021 - December 2021"
• [INSERT KEY TOPICS] with "Product Launch, User Satisfaction, Feature Requests"

📥 Example Input: