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The design of a flower bouquet often comprises a manual step of plant selection that follows an artistic style arrangement. Floral choices for a collection are typically founded on visual aesthetic principles that include shape, line, and color of petals. In this paper, we propose a novel framework that instead classifies sentences that describe sentiments and emotions typically conveyed by flowers, and predicts the bouquet content implicitly. Our work exploits the figurative Language of Flowers that formalizes an expandable list of translation records, each mapping a short-text sentiment sequence to a unique flower type we identify with the bouquet center-of-interest. Records are represented as word embeddings we feed into a gated recurrent neural-network, and a discriminative decoder follows to maximize the score of the lead flower and rank complementary flower types based on their posterior probabilities. Already normalized, these scores directly shape the mix weights in the final arrangement and support our intuition of a naturally formed bouquet. Our quantitative evaluation reviews both stand-alone and baseline comparative results.

Machine Floriography: Sentiment-inspired Flower Predictions over Gated Recurrent Neural Networks