  The surge of social media use brings huge demand of multilingual sentiment
analysis (MSA) for unveiling cultural difference. So far, traditional methods
resorted to machine translation---translating texts in other languages to
English, and then adopt the methods once worked in English. However, this
paradigm is conditioned by the quality of machine translation. In this paper,
we propose a new deep learning paradigm to assimilate the differences between
languages for MSA. We first pre-train monolingual word embeddings separately,
then map word embeddings in different spaces into a shared embedding space, and
then finally train a parameter-sharing deep neural network for MSA. The
experimental results show that our paradigm is effective. Especially, our CNN
model outperforms a state-of-the-art baseline by around 2.1% in terms of
classification accuracy.
