<![CDATA[This research delves into sentiment analysis, employing the transformative capabilities of transformer method. Specifically, leveraging BERT Models, the study aims to advance sentiment classification accuracy by intricately capturing contextual nuances and positive or negative comment on IMDB movies reviews. The transformer architecture's distinctive attention mechanism proves pivotal in comprehending intricate relationships between words, facilitating a profound understanding of sentiment in textual data. Through extensive experimentation, the study establishes the transformative prowess of these methods, showcasing their effectiveness in achieving state-of-the-art results in sentiment analysis tasks. This investigation not only contributes to the evolving landscape of sentiment analysis but also underscores the significance of transformer-based approaches in deciphering the subtleties of human expression within textual data specially for Bert models. This research will predict sentiment analysis on comment of IMDB movies and shows some results which are 3% of loss, 60% off loss validation, 98% of accuracy and 90% of validation accuracy]]>
