SayPro Topic Extraction Reports (compiled data sets from GPT)

SayPro Topic Extraction Reports

(Compiled Data Sets from GPT)

Purpose:
To analyze large volumes of text (e.g., customer feedback, survey responses, social media comments, documents) and automatically extract the main topics and themes for easier insight generation.

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Typical Components of a SayPro Topic Extraction Report:

1. Source Data Summary
   • Description of the text dataset (e.g., number of documents, total word count, time period)
   • Data source(s) (e.g., customer reviews, internal emails, support tickets)
2. Methodology
   • Model and tools used (e.g., GPT-4, fine-tuned topic extraction models)
   • Preprocessing steps (tokenization, cleaning, deduplication)
   • Extraction approach (unsupervised clustering, keyword extraction, summarization)
3. Extracted Topics
   • List of main topics/themes with representative keywords or phrases
   • Topic labels/names for easy identification
   • Topic frequency or prominence in the dataset
4. Topic Relationships
   • How topics correlate or cluster together
   • Visualizations (e.g., topic maps, word clouds, network graphs)
5. Sample Representative Texts
   • Example excerpts or quotes from the dataset that best illustrate each topic
6. Insights & Recommendations
   • Key findings from the topic analysis
   • Suggestions based on emerging themes (e.g., product improvements, customer sentiment)
7. Appendix / Raw Data
   • Full list of extracted keywords or phrases
   • Metadata and additional stats

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Example Output Snippet:

Topic ID	Topic Label	Keywords	Frequency (%)	Sample Excerpt
1	Product Quality	durability, defect, reliable	27%	“The new model is very durable and works reliably.”
2	Customer Service	support, response time, helpful	18%	“Customer support was quick and very helpful.”
3	Pricing Concerns	expensive, cost, value	15%	“The price is a bit high compared to competitors.”