  People communicate in more than 7,000 languages around the world, with around
780 languages spoken in India alone. Despite this linguistic diversity,
research on Sentiment Analysis has predominantly focused on English text data,
resulting in a disproportionate availability of sentiment resources for
English. This paper examines the performance of transformer models in Sentiment
Analysis tasks across multilingual datasets and text that has undergone machine
translation. By comparing the effectiveness of these models in different
linguistic contexts, we gain insights into their performance variations and
potential implications for sentiment analysis across diverse languages. We also
discuss the shortcomings and potential for future work towards the end.
