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The COVID-19 pandemic has essentially transformed the way of leading a life for millions of people across the world. As offices remained closed for months, employees expressed conflicting sentimental analysis on the workfrom-home culture. People worldwide use social media platforms such as Twitter to talk about their daily lives made a trend in the online platform. This research study aims to gauge the public's sentiment on working from home/remote locations during the COVID-19 pandemic by tracking their opinions on Twitter. The existing random forest model trained the data faster but failed to predict the results faster. Therefore, an ensemble model is proposed to predict an outcome using a distinct modeling algorithm. An ensemble classifier has been used for enhancing the performances using the base learning classifiers such as Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR) form an Ensemble classifier. The proposed ensemble model aggregates each base model for the prediction and results for the unseen data. These tokens are then passed to the Ensemble classifier that classifies the sentiments and assigns a polarity (positive, negative, neutral) to every tweet. The proposed Ensemble method improve the average prediction performance over any contributing member in the ensemble. The results obtained by the proposed Ensemble model reached accuracy of 97.47% when compared to the existing models such as Deep LSTM, SVM model that obtained accuracy of 83%, 84.46%.