Sentiment analysis, an essential aspect of natural language processing, plays a crucial role in understanding public opinion, consumer behavior, and market trends. Valence Aware Dictionary and Sentiment Reasoner (VADER) is a widely used tool for sentiment analysis in English. However, its effectiveness in Bengali is limited due to the lack of comprehensive lexicons. This research compares the performance of Bengali VADER and English VADER using 8,660 bilingual sentences. Sentiment scores were generated for each sentence using both implementations, and evaluation metrics were used to assess the performance of these tools. The results indicated a positive correlation (r = 0.5) between the sentiment scores, with most absolute differences falling within a low range, suggesting a high level of agreement. Nevertheless, notable discrepancies were observed, particularly in the handling of complex sentences, negation, and the absence of words. To address these issues, we updated the lexicon and improved the handling of Bengali linguistic features. Following these updates, we conducted a case study with a number of sentences and achieved remarkable results, underscoring the importance of developing more robust sentiment analysis tools for the Bengali language.