Abstract: Sentiment analysis and opinion mining have become
emerging topics of research in recent years but most of the work
is focused on data in the English language. A comprehensive
research and analysis are essential which considers multiple
languages, machine translation techniques, and different classifiers.
This paper presents, a comparative analysis of different approaches
for multilingual sentiment analysis. These approaches are divided
into two parts: one using classification of text without language
translation and second using the translation of testing data to a
target language, such as English, before classification. The presented
research and results are useful for understanding whether machine
translation should be used for multilingual sentiment analysis or
building language specific sentiment classification systems is a better
approach. The effects of language translation techniques, features,
and accuracy of various classifiers for multilingual sentiment analysis
is also discussed in this study.