Social media platforms such as Twitter and Facebook are hugely popular websites through
which Internet users can communicate and spread information worldwide. On Twitter, messages (tweets) are generated by users from all over the world in many different languages.
Tweets about different events almost always encode some degree of sentiment. As is often the
case in the field of language processing, sentiment analysis tools exist primarily in English, so
if we want to understand the sentiment of the original tweets, we are forced to translate them
from the source language into English and pushing the English translations through a sentiment
analysis tool.
However, Lohar et al. (2017) demonstrated that using freely available translation tools often
caused the sentiment encoded in the original tweet to be altered. As a consequence, they built
a series of sentiment-specific translation engines and pushed tweets containing either positive,
neutral or negative sentiment through the appropriate engine to improve sentiment preservation
in the target language. For certain tasks, maintaining sentiment polarity in the target language
during the translation process is arguably more important than the absolute translation quality
obtained. In the work of Lohar et al. (2017), a small drop off in translation quality per se was
deemed tolerable. In this work, we focus on maintaining the level of sentiment preservation
while trying to improve translation quality still further. We propose a nearest sentiment classcombination method to extend the existing sentiment-specific translation systems by adding
training data from the nearest-sentiment class. Our experimental results on German-to-English
reveal that our approach is capable of achieving a proper balance between translation quality
and sentiment preservation.