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███████╗███████╗███╗ ███╗███████╗██╗ ██╗ █████╗ ██╗ ██╔════╝██╔════╝████╗ ████║██╔════╝██║ ██║██╔══██╗██║ ███████╗█████╗ ██╔████╔██║█████╗ ██║ ██║███████║██║ ╚════██║██╔══╝ ██║╚██╔╝██║██╔══╝ ╚██╗ ██╔╝██╔══██║██║ ███████║███████╗██║ ╚═╝ ██║███████╗ ╚████╔╝ ██║ ██║███████╗ ╚══════╝╚══════╝╚═╝ ╚═╝╚══════╝ ╚═══╝ ╚═╝ ╚═╝╚══════╝ DESCRIPTION In this task, you are given a textual dialogue i.e. a user utterance along with two turns of context, you have to classify the emotion of user utterance as one of the emotion classes: Happy, Sad, Angry or Others. REQUIREMENTS ° python 3 ° pandas ° numpy ° tensorflow ° scikit-learn ° keras ° nltk ° ekphrasis ° tweet tokenizer (pip install git+https://github.com/erikavaris/tokenizer.git) ° glove data (http://nlp.stanford.edu/data/glove.840B.300d.zip) need to be extracted in the folder data USAGE python train.py -config configuration.config CONTRIBUTORS Sirine Kéfi Carine Zhang Thibaud Chominot Hussem Ben Belgacem Guillaume Drapala-Bizouarn Data provided by Microsoft
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Contextual Emotion Detection in Text
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