Benchmarking Machine Translated Sentiment Analysis for Arabic Tweets

Traditional approaches to Sentiment Analysis (SA) rely on large annotated data sets or wide-coverage sentiment lexica, and as such often perform poorly on under-resourced languages. This paper presents empirical evidence of an efficient SA approach using freely available machine translation (MT) systems to translate Arabic tweets to English, which we then label for sentiment using a state-of-theart English SA system. We show that this approach significantly outperforms a number of standard approaches on a gold-standard heldout data set, and performs equally well compared to more cost-intense methods with 76% accuracy. This confirms MT-based SA as a cheap and effective alternative to building a fully fledged SA system when dealing with under-resourced languages.