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6 CONCLUSIONS
In this work, we have demonstrated the plausible po-
tential in composing a bouquet made of a lead and
supporting flowers by attending to a set of unquali-
fied sentiment expressions we stream over RNN. We
confirmed that GRU-based RNN improved our flo-
wer prediction quality by about ten percentage points
compared to a standard RNN, and feeding RNN with
spatiotemporal sentiment context prove particularly
beneficial to the performance of short-text sequences.
Yet using bidirectional propagation of sentiment word
vectors that enter RNN was less instinctive, and con-
tributed to an inconsequential prediction gain. Our
proposed simple workflow offers on average high pre-
diction recall to hundreds of mix choices for a main-
line bouquet, and sends a more cohesive emotional
message that is made of semantically related senti-
ments.
To the extent of our knowledge, the work we pre-
sented is first to apply computational linguistic mo-
deling to the language of flowers. We contend that
floriography is an important NLP discipline to pur-
sue from both its rooted historical impact on society
culture, and the prospect to influence areas of critical
theory and sentiment analysis. In its current state, the
corpus we used in this paper is small and challenging,
but we anticipate the language to expand sentiment
translation to thousands of flower plants and further
merit our statistically reasoned system. A direct pro-
gression of our work is to evolve to a task that matches
flower-sentiment pairs from unstructured full text and
not just from a set of prescribed sentiment phrases,
and have a profound practical importance to impact
a much broader scope of application domains that in-
clude cryptography and secured communication.
ACKNOWLEDGEMENTS
We would like to thank the anonymous reviewers for
their insightful suggestions and feedback.
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