A guide on how to use OpenAI embeddings and clustering techniques to analyze survey data and extract meaningful topics and actionable insights from the responses.
The process involves transforming textual survey responses into embeddings, grouping similar responses through clustering, and then identifying key themes or topics to aid in business improvement.
The process involves transforming textual survey responses into embeddings, grouping similar responses through clustering, and then identifying key themes or topics to aid in business improvement.
Unlock advanced customer segmentation techniques using LLMs, and improve your clustering models with advanced techniques
Word embeddings are suitable for use with neural network language models (as will be discussed later); they can also be used to enhance conventional (MEMM, CRF) models. The best ways to incorporate embeddings into such feature-based language models are still being explored. The simplest approach involves the direct use of the vector components as features (Turian et al 2010, Word Representations: A Simple and General Method for Semi-Supervised Learning, ACL 2010; Nguyen and Grishman, ACL 2014). Less direct approaches include building clusters from the embeddings and then using the clusters as features, or selecting prototypical examples of each type and then using similarity to these prototypes (based on embedding similarity) as features. Early results on NE tagging indicate a small advantage for the indirect methods (Guo et al., Revisiting embedding features for simple semi-supervised learning, EMNLP 2014). Models based on word embeddings are producing the best performance on named entity recognition (A. Passos et al, Lexicon Infused Phrase Embeddings for Named Entity Resolution, CoNLL 2014) and are effective for chunking (Turian et al ACL 2010).
