What is an example of using Sentence Transformers for analyzing survey responses or customer feedback by clustering similar feedback comments?

Sentence Transformers can be effectively used to cluster similar survey responses or customer feedback by converting text into numerical embeddings and grouping them based on semantic similarity. These models, such as all-MiniLM-L6-v2, generate dense vector representations that capture the meaning of sentences. By embedding feedback comments into vectors, developers can apply clustering algorithms like K-means or DBSCAN to identify groups of comments with related themes. This approach helps organizations uncover common patterns in unstructured feedback without manual tagging, enabling faster analysis of large datasets.

For example, consider a scenario where a company collects 1,000 open-ended responses about a new product. First, the text data is preprocessed (e.g., removing duplicates, handling typos). Using Sentence Transformers, each comment is converted into a 384-dimensional vector. These embeddings are then clustered using K-means, where the optimal number of clusters can be determined using metrics like the silhouette score. If the algorithm identifies a cluster with comments like “battery life is too short,” “device dies quickly,” and “needs frequent charging,” these can be grouped as a single theme around battery performance. Another cluster might include feedback about “difficult setup process” or “complicated instructions,” highlighting usability issues. This automated grouping allows teams to prioritize fixes based on recurring issues.

Developers can implement this workflow using libraries like sentence-transformers and scikit-learn. Here’s a simplified outline:

1. Load the model: model = SentenceTransformer('all-MiniLM-L6-v2')
2. Generate embeddings: embeddings = model.encode(feedback_list)
3. Cluster with K-means: kmeans = KMeans(n_clusters=5).fit(embeddings)
4. Analyze clusters by sampling comments from each group. To refine results, techniques like dimensionality reduction (e.g., UMAP) can visualize clusters, while adjusting hyperparameters like distance thresholds in DBSCAN can handle varying comment lengths. This method scales well for large datasets and provides actionable insights, such as identifying top customer complaints or tracking sentiment shifts over time.