Automated Discovery of Product Feature Inferences Within Large-Scale Implicit Social Media Data

Abstract

Recently, social media has emerged as an alternative, viable source to extract large-scale, heterogeneous product features in a time and cost-efficient manner. One of the challenges of utilizing social media data to inform product design decisions is the existence of implicit data such as sarcasm, which accounts for 22.75% of social media data, and can potentially create bias in the predictive models that learn from such data sources. For example, if a customer says "I just love waiting all day while this song downloads," an automated product feature extraction model may incorrectly associate a positive sentiment of "love" to the cell phone's ability to download. While traditional text mining techniques are designed to handle well-formed text where product features are explicitly inferred from the combination of words, these tools would fail to process these social messages that include implicit product feature information. In this paper, we propose a method that enables designers to utilize implicit social media data by translating each implicit message into its equivalent explicit form, using the word concurrence network. A case study of Twitter messages that discuss smartphone features is used to validate the proposed method. The results from the experiment not only show that the proposed method improves the interpretability of implicit messages, but also sheds light on potential applications in the design domains where this work could be extended.