This work tackles the important problem of uncertainty detection in sentiment-laden textual data in response to the growing need for sentiment analysis. The paper stems from the pressing need to quantify and identify uncertainty in user evaluations to improve the accuracy and dependability of sentiment analysis models. Bagging regressor and random forest were chosen for the comprehensive review dataset, and then context-aware, and emotion detection are subjected to sentiment analysis. One aspect of the uncertainty detection research was a methodical performance assessment of every model. The findings showed that bagging regressor and random forest were more effective at identifying uncertainty. The results highlight the superiority of bagging regressor as an uncertainty detection method but also the higher accuracy that random forest achieves when compared to other methods. This paper adds to the continuous development of sentiment analysis methodologies by offering a deeper knowledge of uncertainty detection strategies. It also emphasizes the need to use Random Forest and Bagging Regressor for robust uncertainty detection in textual data. The paper provides a springboard for future research initiatives like exploring hybrid models that amalgamate the strengths of different techniques and can potentially yield a more robust and versatile uncertainty detection framework.