A character-based convolutional neural network for language-agnostic Twitter sentiment analysis

Abstract

Most work on tweet sentiment analysis is mono-lingual and the models that are generated by machine learning strategies do not generalize across multiple languages. Cross-language sentiment analysis is usually performed through machine translation approaches that translate a given source language into the target language of choice. Machine translation is expensive and the results that are provided by theses strategies are limited by the quality of the translation that is performed. In this paper, we propose a language-agnostic translation-free method for Twitter sentiment analysis, which makes use of deep convolutional neural networks with character-level embeddings for pointing to the proper polarity of tweets that may be written in distinct (or multiple) languages. The proposed method is more accurate than several other deep neural architectures while requiring substantially less learnable parameters. The resulting model is capable of learning latent features from all languages that are employed during the training process in a straightforward fashion and it does not require any translation process to be performed whatsoever. We empirically evaluate the efficiency and effectiveness of the proposed approach in tweet corpora based on tweets from four different languages, showing that our approach comfortably outperforms the baselines. Moreover, we visualize the knowledge that is learned by our method to qualitatively validate its effectiveness for tweet sentiment classification.