Drawing Insights from Customer Feedback Using NLP
Companies are frequently faced with large amounts of unstructured text data, like product reviews, customer feedback, or comments on social media. Understanding these data can help target marketing efforts by revealing what your customers care about most, but it can be time-consuming to read through comments, and keyword matching frequently misses critical nuances. We'll discuss how we've approached this problem at Google using Natural Language Processing, with examples of the approach applied to open datasets. We'll explore how this fits into the ML project lifecycle, with examples of common pitfalls. Finally, we'll highlight how to use this technology as part of a "human in the loop" approach to supercharge your existing team members.
Key Takeaways: • Pipeline for effective, open source text clustering • Investigation using open source data set • Discussion of how it’s useful for your marketing teams"
Peter Grabowski is a longtime Googler and former Nest employee. He's currently the manager of the Enterprise Machine Learning team in Austin. Previously, he managed a data engineering team at Nest and helped build the Assistant for Kids team at Google. Outside of Google, he teaches machine learning as part of UC Berkeley's Master's in Data Science, and is a managing partner of PXN Residential, LLC.