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.