Comparative Study of Sentiment Classification for Automated Translated Latin Reviews Into Arabic

The majority of available sentiment analysis systems treat English text, which resulted in a significant number of resources to assist researchers in this field. Unfortunately, this is not the case for other languages and, in particular, for the Arabic language. Few serious research attempts are reported to build necessary resources such as datasets to serve Arabic sentiment analysis research efforts. In order to expedite building necessary huge datasets in Arabic or any other dataset-poor language, automatic translation from a rich source-language can make a viable alternative to generate the required resources for a target language. This is mainly driven by the assumption that translation usually preserves the sentiment upto certain degree. In this work, we study the viability of this approach to automatically translate a huge dataset from English to Arabic for sentiment analysis purposes. Experimental results show that sentiment analysis of both original (English) dataset and the translated one (Arabic) produce comparable results. As a result, automatically translated English datasets shall add to the required resources for building robust Arabic sentiment analysis systems. Although automated translation may yield poor translations in terms of readability and comprehension by humans, sentiment analysis systems can still predict the majority of correct sentiments. Such sentiment is preserved by key words. We believe automated translation may be considered as a viable option to produce rich and well represented datasets in order to develop robust and efficient Arabic sentiment analyzers.