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# + [markdown] id="1yKhLP03hArF"
# As we are not developing a chit chat but a virtual conversation between a user and an expert, we will make some assumptions:
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# •	The user will not use emoji or abbreviations in the same sentence with an important request.
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# •	The user might use emoji/abbreviations as a reaction/reply or in a follow up discussion.
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# •	The user might use emoji/abbreviations in a more complex input and a reply might be regarded as emotional and thus might help the conversation going forward.
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# Taking this into consideration, we will implement a secondary flow that will have specific answers/policies and can exist together with the main flow, or standalone if no other input is provided by the user. 
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# Important: Abbreviations that are not considered as specific to chit chat needs will be treated in the main flow, together with the sentence-intent. For example: abbreviation for institutes that are NER.
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# + id="9QrCI1_cg6TF"
# IN: inputs from Auto-correctand Procesing I
# OUT: send identified emoji/abbreviations to the following pipelines: Untrained NIU/Reaction Analysis

# + [markdown] id="HK9_k_wXhXjI"
# Objectives:
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# •	Gathering the emoji/abbreviations from auto-correct, processing and mark them for Untrained NIU/Reactions;
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# Language specificities: no;
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# Dependencies: Auto-correct /Processing I/Untrained NIU/ Reaction analysis; 
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# Database/ Vocabularies needed: Emoji/Abbreviations vocabularies;
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# + id="KFSoniTkhqp6"
# To dos:
# 1.	Mark sentences with emoji/abbreviations for Untrained answers.
# 2.	Classify all the emoji/abbreviations in 6 categories (sad, blink, kiss, smile, cool, laughing out loud).
# 3.	Sad and Smile will be sent to the Reaction analysis pipeline. 
# 4.	The rest will be sent to Untrained NIU.


# + [markdown] id="0ouZs8mAhzeR"
# Use adapted code
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# Code example, but please adapt to the objectives
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# + id="E44ouNp-jnBK"
# https://github.com/amanchadha/coursera-deep-learning-specialization/blob/master/C5%20-%20Sequence%20Models/Week%202/Emojify/Emojify%20-%20v2.ipynb


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