There is a growing interest in mining opinions using sentiment
analysis methods from sources such as news, blogs and
product reviews. Most of these methods have been developed
for English and are difficult to generalize to other languages.
We explore an approach utilizing state-of-the-art machine
translation technology and perform sentiment analysis
on the English translation of a foreign language text. Our experiments
indicate that (a) entity sentiment scores obtained
by our method are statistically significantly correlated across
nine languages of news sources and five languages of a parallel
corpus; (b) the quality of our sentiment analysis method
is largely translator independent; (c) after applying certain
normalization techniques, our entity sentiment scores can be
used to perform meaningful cross-cultural comparisons.