  Sentiment analysis is a widely studied NLP task where the goal is to
determine opinions, emotions, and evaluations of users towards a product, an
entity or a service that they are reviewing. One of the biggest challenges for
sentiment analysis is that it is highly language dependent. Word embeddings,
sentiment lexicons, and even annotated data are language specific. Further,
optimizing models for each language is very time consuming and labor intensive
especially for recurrent neural network models. From a resource perspective, it
is very challenging to collect data for different languages.
  In this paper, we look for an answer to the following research question: can
a sentiment analysis model trained on a language be reused for sentiment
analysis in other languages, Russian, Spanish, Turkish, and Dutch, where the
data is more limited? Our goal is to build a single model in the language with
the largest dataset available for the task, and reuse it for languages that
have limited resources. For this purpose, we train a sentiment analysis model
using recurrent neural networks with reviews in English. We then translate
reviews in other languages and reuse this model to evaluate the sentiments.
Experimental results show that our robust approach of single model trained on
English reviews statistically significantly outperforms the baselines in
several different languages.
