# FeedbackMap: a tool for making sense of open-ended survey responses

arXiv: https://arxiv.org/abs/2306.15112 · HTML: https://mlsanity.com/paper/2306.15112/

## 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 (most cited first, 2 of 2)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)

## Built on by (most cited first, 4 of 4)

- [DesignPref: Capturing Personal Preferences in Visual Design Generation](https://mlsanity.com/paper/2511.20513/) (2025)
- [Human-AI Narrative Synthesis to Foster Shared Understanding in Civic Decision-Making](https://mlsanity.com/paper/2509.19643/) (2025)
- [AudienceView: AI-Assisted Interpretation of Audience Feedback in Journalism](https://mlsanity.com/paper/2407.12613/) (2024)
- [Efficient Personalization of Generative User Interfaces](https://mlsanity.com/paper/2604.09876/) (2026)

## 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.