2020/01/15 by Xiong Zhang, J. Engel, Zhang, Xiong +10
Computer Science · Engineering · #Advanced Text Analysis Techniques #Aggregate (composite) #Computation and Language (cs.CL) #Computer science #Data Visualization and Analytics #Data science #Engineering #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Software Engineering Research #Work (physics) #World Wide Web #cs.CL #cs.HC #cs.LG
paper · pdf · doi:10.48550/arxiv.2001.05171
published in arXiv (Cornell University) (Cornell University) · CHI'20
arxiv created 2020/01/15 · openalex publication_date 2020/01/15 · arxiv updated 2020/01/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
Reviews are integral to e-commerce services and products. They contain a wealth of information about the opinions and experiences of users, which can help better understand consumer decisions and improve user experience with products and services. Today, data scientists analyze reviews by developing rules and models to extract, aggregate, and understand information embedded in the review text. However, working with thousands of reviews, which are typically noisy incomplete text, can be daunting without proper tools. Here we first contribute results from an interview study that we conducted with fifteen data scientists who work with review text, providing insights into their practices and challenges. Results suggest data scientists need interactive systems for many review analysis tasks. In response we introduce Teddy, an interactive system that enables data scientists to quickly obtain insights from reviews and improve their extraction and modeling pipelines.