Developer builds emotion-detection AI tool, finds two-stage model beats single-choice design

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A developer spent a day building 'waif', an emotion analysis tool powered by Jev, a System One model from TypeSafe that processes natural language. The initial design used a single Choice across sixty emotion words but failed because synonyms like 'annoyed', 'irritated', and 'frustrated' represent the same feeling rather than true alternatives. Switching to a two-stage approach — first selecting an emotion family, then a specific shade within it — scored 22 out of 24 in accuracy tests, with both failures occurring at the family-selection stage. The developer also found that published valence-arousal-dominance lexicons performed poorly in context, partly because dominance and valence are highly correlated and negative emotions cluster too closely for nearest-neighbour matching to be reliable. The tool is publicly accessible, with all scoring data and methodology documented on the project's website.

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