With the advent of social media, publicly expressing opinions about businesses are hassle-free. Terms like ‘opinion mining’ and ‘microblogging’ have carved a niche. Organizations’ concern for brand reputation has accentuated the need for understanding customer’s choices and interests. Sentiment analysis scrutinizes users’ opinions through machine learning (ML) and natural language processing (NLP), a subset of which is sarcasm detection. The sarcasm detection database was fetched from Kaggle, containing nearly 30,000 tweet headlines curated for sarcasm detection, having high-quality labels with less noise. On implementing four ML algorithms, it is observed that training and testing data accuracies of random forest are 98.83% and 77.22%, linear support vector is 90.69% and 83.23%, logistic regression is 87.78% and 82.52%, Naive Bayes is 79.77% and 71.69%. This paper aims to build an RNN model based on LSTM architecture to accurately classify any given input as sarcastic or not.