Causal Relationship Detection in Archival Collections of Product Reviews for Understanding Technology Evolution
Causal Relationship Detection in Archival Collections of Product Reviews for Understanding Technology Evolution
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DOI：
10.1145/2937752
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发表时间：
2016-08
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影响因子：
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通讯作者：
Yating Zhang;A. Jatowt;Katsumi Tanaka
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文献类型：
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作者：
Yating Zhang;A. Jatowt;Katsumi Tanaka
技术进步是当今生活方式快速变化背后的关键原因之一。了解产品和物体是如何进化的，不仅可以帮助我们理解社会的进化模式，还可以为有效的产品设计提供线索，并为预测未来提供支持。我们提出了一个总体框架，通过检测因果关系来分析技术对我们生活的影响，其中原因代表技术的变化，而效果是社会生活的变化，例如新的活动或使用产品的新方式。我们通过从长期收集的产品评论中挖掘因果关系，解决了通过“社会影响镜头”观察技术演变的挑战。特别是，我们首先建议将词汇表分为两组：描述产品特性的术语（称为物理术语）和表示产品使用的术语（称为概念术语）。然后，我们搜索与术语外观相关的两种变化：基于频率的变化和基于上下文的变化。前者表示一个词被频繁使用的时期，而后者表示这个词在语境中的高度变化时期。基于检测到的变化，我们然后搜索因果项对，使物理项的变化触发概念项的变化。接下来，我们扩展了我们的方法，以寻找词组之间的因果关系，例如代表相同技术并引起给定概念变化的一组单词，或者代表同时“共同导致”概念变化的两种不同技术的一组单词。我们使用1995年至2013年的亚马逊产品评论数据集对不同的产品类型进行了实验，我们证明了我们的方法优于最先进的基线。
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.