  The introduction of emojis (or emoticons) in social media platforms has given
the users an increased potential for expression. We propose a novel method
called Classification of Emojis using Siamese Network Architecture (CESNA) to
learn emoji-based representations of resource-poor languages by jointly
training them with resource-rich languages using a siamese network.
  CESNA model consists of twin Bi-directional Long Short-Term Memory Recurrent
Neural Networks (Bi-LSTM RNN) with shared parameters joined by a contrastive
loss function based on a similarity metric. The model learns the
representations of resource-poor and resource-rich language in a common emoji
space by using a similarity metric based on the emojis present in sentences
from both languages. The model, hence, projects sentences with similar emojis
closer to each other and the sentences with different emojis farther from one
another. Experiments on large-scale Twitter datasets of resource-rich languages
- English and Spanish and resource-poor languages - Hindi and Telugu reveal
that CESNA outperforms the state-of-the-art emoji prediction approaches based
on distributional semantics, semantic rules, lexicon lists and deep neural
network representations without shared parameters.
