FeedbackMap: a tool for making sense of open-ended survey responses
This tool addresses a problem for social scientists, non-profits, and educational institutions, but it appears incremental as it applies existing NLP methods to a specific domain without major breakthroughs.
The paper tackles the challenge of analyzing open-ended survey responses by introducing FeedbackMap, a web-based tool that uses natural language processing to generate summaries, identify examples, and visualize responses, though it does not report specific performance metrics or numbers.
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.