  Adversarial attacks are a type of attack on machine learning models where an
attacker deliberately modifies the inputs to cause the model to make incorrect
predictions. Adversarial attacks can have serious consequences, particularly in
applications such as autonomous vehicles, medical diagnosis, and security
systems. Work on the vulnerability of deep learning models to adversarial
attacks has shown that it is very easy to make samples that make a model
predict things that it doesn't want to. In this work, we analyze the impact of
model interpretability due to adversarial attacks on text classification
problems. We develop an ML-based classification model for text data. Then, we
introduce the adversarial perturbations on the text data to understand the
classification performance after the attack. Subsequently, we analyze and
interpret the model's explainability before and after the attack
