Sarcasm refers to the use of words that have different literal and intended meanings. It represents the usage of words that are opposite of what is literally said, especially in order to insult, mock, criticise or irritate someone. These types of statements may be funny or amusing to others but may hurt or annoy the person towards whom it is intended. Identification of sarcastic phrases from social media posts finds its application in different domains like sentiment analysis, opinion mining, author profiling, and harassment detection. We have proposed a model for the shared task iSarcasmEval - Intended Sarcasm Detection in English and Arabic (CITATION) by SemEval-2022 considering the language English based on ELmo embeddings for Subtasks A and C and TF-IDF vectors and Gaussian Naive bayes classifier for Subtask B. The proposed model resulted in a F1 score 0.2012 for sarcastic texts in Subtask A, macro-F1 score of 0.0387 and 0.2794 for Subtasks B and C respectively.