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"}
]
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2. SayPro Validate Extracted Topics
Validation Criteria:
- Relevance: Does the topic align with SayProโs 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}
]
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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"
}
]
๐ SayPro Integration Into SayPro CMS
After categorization:
- Index topics in the CMS database.
- Tag existing content with validated categories.
- Enable search & filtering in the SayPro front-end using these topic tags.
- Track analytics (engagement, search frequency) to refine the taxonomy over time.