SayPro Develop and run sentiment analysis using GPT on SayPro feedback.

Step 1: Prepare Your Data

• Collect raw feedback text from SayPro’s various channels (surveys, social media, emails, reviews).
• Clean the data: remove duplicates, irrelevant info, and anonymize personal details.

Step 2: Design GPT Prompts for Sentiment Analysis

• Use clear instructions to classify sentiment into categories: Positive, Neutral, Negative.
• Optionally, request an emotion label (e.g., Joy, Sadness, Anger, Surprise).

Example Prompt for Sentiment Classification:

pgsqlCopyEditPlease classify the sentiment of the following customer feedback as Positive, Neutral, or Negative. Also provide a brief explanation for the classification.

Feedback: "I really enjoyed the eLearning course, the content was clear and engaging."

Expected GPT Response:

makefileCopyEditSentiment: Positive
Explanation: The feedback expresses enjoyment and compliments the content quality, indicating positive sentiment.

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Step 3: Batch Processing

• Automate feeding batches of feedback entries into GPT (via API or interface).
• Store the sentiment and explanation results linked to each feedback.

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Step 4: Aggregate & Analyze Results

• Calculate sentiment distribution percentages.
• Identify common themes or emotional trends.
• Compare sentiment by service line, time period, or demographics.

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Step 5: Use Insights to Inform Strategy

• Share reports with teams.
• Track progress over time.
• Refine messaging and service delivery accordingly.