Teachers often conduct surveys in order to collect data from a predefined
group of students to gain insights into topics of interest. When analyzing
surveys with open-ended textual responses, it is extremely time-consuming,
labor-intensive, and difficult to manually process all the responses into an
insightful and comprehensive report. In the analysis step, traditionally, the
teacher has to read each of the responses and decide on how to group them in
order to extract insightful information. Even though it is possible to group
the responses only using certain keywords, such an approach would be limited
since it not only fails to account for embedded contexts but also cannot detect
polysemous words or phrases and semantics that are not expressible in single
words. In this work, we present a novel end-to-end context-aware framework that
extracts, aggregates, and abbreviates embedded semantic patterns in
open-response survey data. Our framework relies on a pre-trained natural
language model in order to encode the textual data into semantic vectors. The
encoded vectors then get clustered either into an optimally tuned number of
groups or into a set of groups with pre-specified titles. In the former case,
the clusters are then further analyzed to extract a representative set of
keywords or summary sentences that serve as the labels of the clusters. In our
framework, for the designated clusters, we finally provide context-aware
wordclouds that demonstrate the semantically prominent keywords within each
group. Honoring user privacy, we have successfully built the on-device
implementation of our framework suitable for real-time analysis on mobile
devices and have tested it on a synthetic dataset. Our framework reduces the
costs at-scale by automating the process of extracting the most insightful
information pieces from survey data.

本研究提出了一种新颖的端到端的上下文感知框架，该框架可提取、聚合和缩略嵌入式语义模式的开放响应调查数据，在识别度上优于传统的基于关键词的方法。利用预训练的自然语言模型对文本数据进行编码，将其转化为语义向量，并通过聚类的方式将向量划分到若干组并提取关键词或总结句，为每一组提供上下文感知的词云，并遵守用户隐私的设计。该方案可应用于移动设备的实时分析，减轻了对来自调查数据的信息提取成本。