FeedbackMap: a tool for making sense of open-ended survey responses
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 2 of its 18 references that are in the map. First posted on arXiv 26 Jun 2023.
Builds on
2 of 18 references.
References · 2
Built on by
Citing papers per year: 2024: 1; 2025: 2; 2026: 1.
4 papers.
- 2025DesignPref: Capturing Personal Preferences in Visual Design Generationcited by 7
- 2025Human-AI Narrative Synthesis to Foster Shared Understanding in Civic Decision-Makingcited by 1
- 2024AudienceView: AI-Assisted Interpretation of Audience Feedback in Journalismcited by 0
- 2026Efficient Personalization of Generative User Interfacescited by 0
- 2026Efficient Personalization of Generative User Interfaces10 Apr 2026
- 2025DesignPref: Capturing Personal Preferences in Visual Design Generation25 Nov 2025
- 2025Human-AI Narrative Synthesis to Foster Shared Understanding in Civic Decision-Making23 Sep 2025
- 2024AudienceView: AI-Assisted Interpretation of Audience Feedback in Journalism17 Jul 2024