Analyzing open-ended survey responses is a crucial yet challenging task for social scientists, non-profit organizations, and educational institutions, as they often face the trade-off between obtaining rich data and the burden of reading and coding textual responses. This demo introduces FeedbackMap, a web-based tool that uses natural language processing techniques to facilitate the analysis of open-ended survey responses. FeedbackMap lets researchers generate summaries at multiple levels, identify interesting response examples, and visualize the response space through embeddings. We discuss the importance of examining survey results from multiple perspectives and the potential biases introduced by summarization methods, emphasizing the need for critical evaluation of the representation and omission of respondent voices.

翻译：分析开放式调查回应是社会科学家、非营利组织和教育机构面临的一项关键但具有挑战性的任务，因为它们常常需要在获取丰富数据与阅读及编码文本回应的负担之间做出权衡。本演示介绍了FeedbackMap，这是一个基于网络的工具，利用自然语言处理技术来促进开放式调查回应的分析。FeedbackMap让研究者能够生成多层次的摘要、识别有趣的回应示例，并通过嵌入可视化回应空间。我们探讨了从多角度审视调查结果的重要性以及摘要方法可能引入的潜在偏差，强调需批判性评估受访者声音的表征与缺失。