  Social media companies as well as authorities make extensive use of
artificial intelligence (AI) tools to monitor postings of hate speech,
celebrations of violence or profanity. Since AI software requires massive
volumes of data to train computers, Machine Translation (MT) of the online
content is commonly used to process posts written in several languages and
hence augment the data needed for training. However, MT mistakes are a regular
occurrence when translating sentiment-oriented user-generated content (UGC),
especially when a low-resource language is involved. The adequacy of the whole
process relies on the assumption that the evaluation metrics used give a
reliable indication of the quality of the translation. In this paper, we assess
the ability of automatic quality metrics to detect critical machine translation
errors which can cause serious misunderstanding of the affect message. We
compare the performance of three canonical metrics on meaningless translations
where the semantic content is seriously impaired as compared to meaningful
translations with a critical error which exclusively distorts the sentiment of
the source text. We conclude that there is a need for fine-tuning of automatic
metrics to make them more robust in detecting sentiment critical errors.
