SayPro Compile, validate, and categorize extracted topics within the SayPro content management framework

1. SayPro Compile Extracted Topics

Sources to Compile From:

• Course modules and training material
• User-generated content (forums, comments)
• Internal documentation
• Knowledge bases or uploaded files
• Metadata from SayPro content objects (titles, tags, descriptions)

Techniques:

• NLP Topic Modeling: Use LDA (Latent Dirichlet Allocation), BERTopic, or transformer-based models to extract dominant topics.
• Keyword Extraction: Use TF-IDF, RAKE, or KeyBERT to extract keywords or key phrases.
• Named Entity Recognition (NER): Identify named topics like organizations, systems, methodologies, etc.

Output Example:

jsonCopy code[
  {"topic": "Workplace Safety", "source": "Module 2"},
  {"topic": "Digital Literacy", "source": "User Forum"},
  {"topic": "Entrepreneurship", "source": "Curriculum Outline"}
]

---

✅ 2. SayPro Validate Extracted Topics

Validation Criteria:

• Relevance: Does the topic align with SayPro’s objectives (e.g., skill development, professional training)?
• Frequency: How often is the topic mentioned or emphasized?
• Contextual Accuracy: Is the topic used in the correct context?
• Duplication Check: Are similar or synonymous topics grouped or standardized?

Techniques:

• Manual Sampling: Validate a sample set with SMEs (Subject Matter Experts).
• Semantic Similarity Scoring: Use cosine similarity (e.g., Sentence-BERT) to merge or flag overlapping topics.
• Taxonomy Matching: Cross-reference with existing SayPro topic taxonomies or frameworks (if available).

Validated Output Example:

jsonCopy code[
  {"topic": "Occupational Health & Safety", "validated": true},
  {"topic": "Entrepreneurship", "validated": true},
  {"topic": "Online Collaboration Tools", "validated": false}
]

---

✅ 3. SayPro Categorize Topics

Categorization Dimensions:

• Domain Category: e.g., Business, Technology, Health, Education
• Skill Level: Beginner, Intermediate, Advanced
• Learning Path: e.g., Core, Elective, Supplementary
• Content Type: Video, Assessment, Course, Case Study

Methods:

• Ontology or Taxonomy Mapping: Map topics to predefined SayPro content categories.
• Clustering: Group similar topics into clusters using ML algorithms.
• Rule-based Classification: If topics follow naming patterns or prefixes.

Categorized Output Example:

jsonCopy code[
  {
    "topic": "Workplace Safety",
    "category": "Health & Safety",
    "skill_level": "Beginner",
    "content_type": "Course"
  },
  {
    "topic": "Digital Literacy",
    "category": "Technology",
    "skill_level": "Intermediate",
    "content_type": "Assessment"
  }
]

---

🔁 SayPro Integration Into SayPro CMS

After categorization:

1. Index topics in the CMS database.
2. Tag existing content with validated categories.
3. Enable search & filtering in the SayPro front-end using these topic tags.
4. Track analytics (engagement, search frequency) to refine the taxonomy over time.