Example of Using Sentence Transformers for Clustering Survey Responses To analyze survey responses or customer feedback, Sentence Transformers can generate dense vector representations (embeddings) of text, enabling clustering of semantically similar comments. For instance, a company might collect thousands of open-ended feedback entries. By converting these comments into embeddings, you can group them into clusters representing common themes (e.g., "shipping delays" or "product quality issues") without manual labeling.
Step-by-Step Implementation
- Embedding Generation: Use a pre-trained model like
all-MiniLM-L6-v2from Sentence Transformers to convert text into embeddings. For example, the comment “Delivery took too long” and “Shipping was slow” would produce vectors close in the embedding space. - Clustering: Apply algorithms like K-means or DBSCAN to group embeddings. K-means requires specifying the number of clusters (e.g.,
k=5), while DBSCAN automatically detects clusters based on density. - Analysis: Extract cluster labels using keywords or representative samples. For instance, a cluster might include comments like “package arrived late” and “delayed shipment,” indicating a shipping-related issue.
Practical Considerations
- Dimensionality Reduction: Use techniques like UMAP or PCA to visualize clusters in 2D/3D for validation.
- Optimization: Experiment with different models (e.g.,
paraphrase-mpnet-base-v2for longer text) and clustering parameters to improve accuracy. - Actionable Insights: Identify recurring issues (e.g., 30% of feedback relates to "customer service wait times") to prioritize improvements.
This approach automates theme discovery, reduces manual effort, and scales to large datasets, making it ideal for businesses analyzing unstructured feedback.