The issue you describe is typically called "Valence shifting" in this specific case "negation processing". It is of course a difficult problem to capture word-l

The issue you describe is typically called "Valence shifting" in this specific case "negation processing". It is of course a difficult problem to capture word-level sentiment and emotions but recent techniques in academic work obtain decent results. However, industry typically relies on sentence- or document-level sentiment in, for instance, customer reviews with systems obtaining 80-90 F1-score which is very good. Often in e-commerce, aspect-based sentiment analysis is used in which a qualifying sentiment is attached to a target aspect, e.g. from a phone review systems extract: battery: large > positive; screen: dim > positive. You might have seen these types of reports in aggregate on review our e-commerce sites yourself. It is however an ongoing field of research to process the scope of negation and uncertainty, but the field is making strides. State-of-the-art attention-based models obtain good scores on benchmark fine-grained sentiment analysis datasets such as the GoodFor/BadFor and MPQA2.0 of around 70% F1score [1]. This performance is nearly enough for commercial systems, depending on how you employ them. 1. https://link.springer.com/article/10.1186/s13673-019-0196-3 https://link.springer.com/article/10.1186/s13673-019-0196-3