Deep Learning Paradigm with Transformed Monolingual Word Embeddings for Multilingual Sentiment Analysis

TL;DR

This paper introduces a novel deep learning framework for multilingual sentiment analysis that maps monolingual embeddings into a shared space, avoiding reliance on machine translation and improving accuracy.

Contribution

It proposes a new paradigm combining monolingual embedding mapping and parameter-sharing neural networks for effective multilingual sentiment analysis.

Findings

CNN model outperforms baseline by 2.1% accuracy

Shared embedding space improves cross-lingual sentiment classification

Method reduces dependency on machine translation quality

Abstract

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.