You are working with a large set of customer feedback collected from surveys, service notes, and digital comments. The goal is to turn unstructured text into findings that business teams can act on, such as recurring complaints, positive drivers, and shifts in sentiment across products or service experiences. Some comments are short and noisy, while others contain multiple issues in the same response. You may also need to separate broad themes like delivery, product quality, pricing, and account support.
Given a dataset of customer feedback, how would you derive actionable insights?
Sentiment analysis for positive, negative, and neutral feedbackTF-IDF for interpretable term weighting and keyword extractionTopic modeling to group recurring complaint and praise themesText classification trade-offs between classical ML and transformers