Sensemaker Claimed

Effectively understanding large-scale public input is a significant challenge, as traditional methods struggle to translate thousands of diverse opinions into actionable insights. ‘Sensemaker’ showcases how Google's Gemini models can be used to transform massive volumes of raw community feedback into clear, digestible insights, aiding the analysis of these complex discussions.

The tools illustrate methods for:

• Topic Identification: identifies the main points of discussion. The level of detail is configurable, allowing the tool to discover: just the top level topics; topics and subtopics; or the deepest level — topics, subtopics, and themes (sub-subtopics).

• Statement Categorization: sorts statements into topics defined by a user or from the Topic Identification function. Statements can belong to more than one topic.

• Summarization: analyzes statements and vote data to output a summary of the conversation, including an overview, themes discussed, and areas of agreement and disagreement.

These methods were applied in a Jigsaw case study in Bowling Green, Kentucky, analyzing a major U.S. digital civic conversation. Please see our document library for a full breakdown of available methods and types.