✅ SayPro Consumer Sentiment and Attribute Analysis Guide
1. Define the Objective
Clarify what you aim to understand:
- Are consumers satisfied or dissatisfied with SayPro services?
- What emotional tone (positive/neutral/negative) dominates feedback?
- Which product or service attributes influence consumer perception?
2. Collect Data
Use multiple channels to gather consumer opinions:
- Surveys (structured questions + open-ended responses)
- Social media mentions (e.g., Facebook, Twitter, Instagram)
- SayPro feedback forms & reviews
- Interviews and focus groups
3. Preprocess the Data
Clean and prepare textual data:
- Remove stop words, special characters, and irrelevant tokens
- Standardise language (e.g., convert slang or abbreviations)
- Tokenise and lemmatise text
4. Conduct Sentiment Analysis
Use manual coding or AI models to determine:
- Polarity: Positive, Negative, or Neutral
- Intensity: Strong, Moderate, Weak
📌 Example AI Model Use: SayPro-defined GPT prompts or a BERT-based sentiment model.
5. Extract Key Attributes
Identify product/service features mentioned, such as:
- Quality
- Price
- Ease of Use
- Support Services
- Accessibility
- Brand Perception
📄 Use SayPro Template 002-AN: Analytical Findings Log to record these insights.
6. Attribute-Sentiment Mapping
Link each attribute to its associated sentiment:
| Attribute | Positive % | Negative % | Neutral % |
|---|---|---|---|
| Support | 75% | 10% | 15% |
| Pricing | 40% | 50% | 10% |
| Accessibility | 60% | 30% | 10% |
7. Generate Insight Summaries
Use SayPro Template 001-RS: Insight Summary to:
- Summarise dominant sentiments
- Highlight priority attributes
- Recommend product/service improvements
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