NLP models can classify text content as positive or negative. Except that, we also need to know the timeliness of such text content. That is whether the text is describing something that has already happened or predicting something that might happen in the future. For example:
Text 1: Stock A has risen more than 7.5%. (positive, but happened already)
Text 2: Stock B might be exposed to downside risk due to its poor financial situation (negative prediction).
It is obvious that Text 2 contains more useful information because it is a prediction of the future while Text 1 is just a description of the past. However, current NLP models, as I understand, cannot recognize such timeliness inside the text content.
Dose anyone have any ideas about this problem? Any introduction of academic journals and surveys would be very useful.