Tutorial: Semantic Clustering of User Messages with LLM Prompts

This blog post demonstrates a faster, more efficient method for analyzing user forum data using Large Language Models (LLMs) instead of traditional data science techniques. The author leverages the power of AI prompts to achieve semantic clustering, significantly reducing the time and effort required.

The process begins with publicly available Discord forum data, specifically tech support threads. This data is pre-processed and formatted into a pandas DataFrame, including a sentiment score based on user feedback (e.g., "thank you"). Dashboards are created to visualize message volumes, user engagement, and satisfaction trends, revealing initial insights. Key findings from this initial exploration include a general correlation between user turns and satisfaction, but a lack of correlation between response time and satisfaction.

The core of the method involves prompting LLMs (specifically Google Gemini and Perplexity AI) to perform the data analysis. The author provides several key prompts:

1. Summary Generation: The LLM generates concise summaries of user messages and identifies high-level conversation topics.
2. Clustering Statistics: The LLM calculates clustering statistics (Silhouette score) to determine the optimal number of clusters.
3. Clustering: The LLM performs the actual clustering using the chosen method and provides cluster labels.
4. Hierarchical Clustering: The LLM performs hierarchical clustering, identifying both high-level and more granular clusters.
5. Visualization Code Generation: The LLM generates Streamlit code to visualize the resulting clusters.

The author experiments with both raw text summaries and numerical embeddings (generated using OpenAI's embedding API) as input for the LLM. The results show that using the LLM's internal embedding generation leads to more accurate and reliable cluster topics, highlighting a key finding: letting the LLM generate its own embeddings is preferable to providing externally generated ones.

The analysis is extended to include data from multiple Discord servers, allowing for cross-vendor comparisons and revealing common user issues. The final visualization effectively showcases these common problems.

The blog post concludes by summarizing the steps involved and providing references to relevant resources, including the research paper that inspired this approach (Clio), the used LLMs, and the embedding model. The overall message is a clear demonstration of how LLMs can significantly streamline the process of extracting meaningful insights from large datasets, replacing more complex data science workflows with simpler, prompt-based methods.

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