Causal Relationship Detection in Archival Collections of Product Reviews for Understanding Technology Evolution.

Technology progress is one of the key reasons behind today's rapid changes in lifestyles. Knowing how products and objects evolve can not only help with understanding the evolutionary patterns in our society but can also provide clues on effective product design and can offer support for predicting the future. We propose a general framework for analyzing technology's impact on our lives through detecting cause--effect relationships, where causes represent changes in technology while effects are changes in social life, such as new activities or new ways of using products. We address the challenge of viewing technology evolution through the “social impact lens” by mining causal relationships from the long-term collections of product reviews. In particular, we first propose dividing vocabulary into two groups: terms describing product features (called physical terms) and terms representing product usage (called conceptual terms). We then search for two kinds of changes related to the appearance of terms: frequency-based and context-based changes. The former indicate periods when a word was significantly more frequently used, whereas the latter indicate periods of high change in the word's context. Based on the detected changes, we then search for causal term pairs such that the change in the physical term triggers the change in the conceptual term. We next extend our approach to finding causal relationships between word groups such as a group of words representing the same technology and causing a given conceptual change or group of words representing two different technologies that simultaneously “co-cause” a conceptual change. We conduct experiments on different product types using the Amazon Product Review Dataset, which spans 1995 to 2013, and we demonstrate that our approaches outperform state-of-the-art baselines.