A comparison of two cutting-edge dynamic topic models solving consumer complaints classification exercise
Customer reviews about products and services provide valuable information about customer satisfaction. They provide insight into what should be improved across the whole product development. Dynamic topic models in business intelligence can identify key product qualities and other satisfaction factors, cluster them into categories, and evaluate how business decisions materialized in customer satisfaction over time. This is highly valuable information not only for product managers.
This article will compare two of the latest topic models to classify customer complaints data. BERTopic by Maarten Grootendorst (2022) and the recent FASTopic by Xiaobao Wu et al. (2024) presented at last year’s NeurIPS, are the current leading models for topic analytics of customer data. For these models, we’ll explore in Python code:
- how to effectively preprocess data
- how to train a Bigram topic model for customer complaint analysis
- how to model topic activity over time.