Detecting valence in sentences and tweets involves determining the polarity—positive, negative, or neutral—of the text. Early sentiment analysis systems often represented textual instances as binary vectors in a feature space to train classifiers. More recently, low-dimensional continuous representations, where vector dimensions take on real-number values rather than just 0 or 1, have significantly improved the classification accuracy of valence detection models.