  Machine learning approaches in sentiment analysis principally rely on the
abundance of resources. To limit this dependence, we propose a novel method
called Siamese Network Architecture for Sentiment Analysis (SNASA) to learn
representations of resource-poor languages by jointly training them with
resource-rich languages using a siamese network.
  SNASA 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 sentence
representations of resource-poor and resource-rich language in a common
sentiment space by using a similarity metric based on their individual
sentiments. The model, hence, projects sentences with similar sentiment closer
to each other and the sentences with different sentiment farther from each
other. Experiments on large-scale datasets of resource-rich languages - English
and Spanish and resource-poor languages - Hindi and Telugu reveal that SNASA
outperforms the state-of-the-art sentiment analysis approaches based on
distributional semantics, semantic rules, lexicon lists and deep neural network
representations without sh
