Abstract: Text style transfer aims to change the style of the input text in graphs to the target style while preserving the content to some extent. In previous work, researchers defined style of a sentence as one or some of its attributes, including but not limited to sentiment, formality, factuality, etc. Hence, the goal has been to change the specified attribute or attributes in the input sentence to the target attribute or attributes. For example, changing a positive sentence to a negative sentence while keeping its key information. However, due to the lack of parallel data and evaluation metric, there has been a slow growth in this field compared to others. To explore this, the paper includes several current experiments and pipelines that have been modified for the purpose. Datasets such as the Yelp Reviews have been used, along with other sentiment analysis dataset, and modified setup and code to get original results and get an understanding on the existing solutions.