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

TL;DR

FeedbackMap is a web-based tool that leverages natural language processing to help researchers analyze, summarize, and visualize open-ended survey responses, addressing challenges in coding and interpretation.

Contribution

The paper introduces FeedbackMap, a novel tool that integrates NLP techniques for multi-level summarization and visualization of survey responses, enhancing qualitative analysis.

Findings

Facilitates efficient analysis of open-ended responses

Enables visualization of response space through embeddings

Highlights potential biases in summarization methods

Abstract

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.