Language-Independent Twitter Classification Using Character-Based Convolutional Networks

Abstract

Most research on Twitter classification is focused on tweets in English. But Twitter supports over 40 languages and about 50% of tweets are non-English tweets. To fully use the Twitter contents, it is important to develop classifiers that can classify multilingual tweets or tweets of mixed languages (for example tweets mainly in Chinese but containing English words). The translation-based model is a classical approach to achieving multilingual or cross-lingual text classification. Recently character-based neural models are shown to be effective for text classification. But they are designed for limited European languages and require identification of languages to build an alphabet to encode and quantize characters. In this paper, we propose UniCNN (Unicode character Convolutional Networks), a fully language-independent character-based CNN model for the classification of tweets in multiple languages and mixed languages, not requiring language identification. Specifically, we propose to encode the sequence of characters in a tweet into a sequence of numerical UTF-8 codes, and then train a character-based CNN classifier. In addition, a character-based embedding layer is included before the convolutional layer for learning distributed character representation. We conducted experiments on Twitter datasets for multilingual sentiment classification in six languages and for mixed-language informativeness classification in over 40 languages. Our experiments showed that UniCNN mostly performed better than state-of-the-art neural models and traditional feature-based models, while not requiring the extra burden of any translation or tokenization.