Text Mining via ChatGPT to Extract Voice of Customer Insights from Twitter Conversational Interactions Dataset

Traditional Voice of Customer (VoC) extraction techniques often fail to fully grasp the depth and subtlety of customer sentiments. Even though modern Machine Learning (ML) methods have made progress, they still face challenges in accurately interpreting the nuances of digital customer communication. This article aims to show the usefulness of OpenAI's GPT-3.5 Turbo, leveraging its advanced Natural Language Processing (NLP) capabilities for extracting VoC information from online customer support interactions on Twitter. This study demonstrates the effective use of GPT-3.5 Turbo in VoC extraction, highlighting its ability to deeply understand conversational context and process data in a more intuitive manner. The model's extensive, multilingual processing ability also allows for a broader and more inclusive VoC analysis. The findings suggest a significant advancement in VoC analysis, paving the way for more insightful, data-driven customer service strategies and improved decision-making in product development and process enhancement.