SayPro sentiment results

Segmenting SayPro Sentiment Results by Service Line

1. Define Each Service Line’s Feedback Pool

• eLearning: Feedback related to online courses, training platforms, LMS usability, course content, instructors.
• Community Development: Comments on outreach programs, local engagement, social projects, empowerment initiatives.
• Research: Sentiment on SayPro’s published reports, studies, research collaborations, transparency.
• Career Services: Feedback on job placement, career counseling, mentorship, internship facilitation.

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2. Data Preparation

• Tag each feedback entry with its corresponding service line, either via metadata, survey question, or text classification.
• Use NLP classifiers or keyword filters if tags aren’t explicit (e.g., “course,” “training” = eLearning; “community,” “village,” “project” = Community Development).

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3. Perform Sentiment Analysis Per Segment

• Use GPT or sentiment tools to assign sentiment labels (Positive, Neutral, Negative) or scores to each feedback entry within each service line.
• Aggregate results per service line.

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4. Example Output Format

Service Line	Total Feedback Entries	Positive (%)	Neutral (%)	Negative (%)	Average Sentiment Score (1-10)
eLearning	3,200	72%	15%	13%	8.1
Community Development	2,500	65%	20%	15%	7.5
Research	1,800	60%	25%	15%	7.2
Career Services	2,500	68%	18%	14%	7.8

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5. Insights & Interpretation

• Identify strengths: e.g., eLearning shows highest positive sentiment reflecting satisfaction with courses.
• Spot weaknesses: e.g., Research feedback has the lowest positive sentiment, indicating potential communication or impact gaps.
• Tailor improvement strategies: Develop targeted action plans per service line based on sentiment trends.

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6. Optional: Drill Down by Themes Within Service Lines

• For example, under eLearning:
  • Usability vs. Content vs. Instructor effectiveness sentiment.
• Under Community Development:
  • Engagement vs. Impact vs. Resource availability.