This paper proposes a sentiment analysis model based on BERT and XLNet for movie reviews. Movie reviews are a valuable source of information for potential viewers, but reading all of them can be time-consuming and overwhelming. Summarizing reviews can help viewers make informed decisions without spending time reading all of them. Sentiment analysis, or opinion mining, can extract subjective information from movie reviews, such as the reviewer's overall opinion, strengths and weaknesses, and recommendations. This information can help viewers decide whether to watch a movie. XLNet and BERT are pre-trained advanced language models that learn bidirectional relationships between words, improving performance on many natural language processing tasks. BERT uses a masked language modeling objective, while XLNet uses a permutation language modeling objective. This experiment is based on the proposed method for XLNet and BERT, using the IMDB dataset of 50K reviews and the Rotten Tomatoes dataset. Both datasets were pre-processed using data cleaning, duplicate removal, lemmatization, and handling of chat words to improve baseline results. The results indicate that XLNet achieved the highest accuracy on both datasets. Sentiment analysis provides insights into how emotions and attitudes are expressed in movie reviews, which can be used to predict a movie's performance based on its overall sentiment. Keywords: XLNet, BERT, Sentiment analysis, IMDB dataset, Rotten Tomatoes dataset, Machine learning.This paper proposes a sentiment analysis model based on BERT and XLNet for movie reviews. Movie reviews are a valuable source of information for potential viewers, but reading all of them can be time-consuming and overwhelming. Summarizing reviews can help viewers make informed decisions without spending time reading all of them. Sentiment analysis, or opinion mining, can extract subjective information from movie reviews, such as the reviewer's overall opinion, strengths and weaknesses, and recommendations. This information can help viewers decide whether to watch a movie. XLNet and BERT are pre-trained advanced language models that learn bidirectional relationships between words, improving performance on many natural language processing tasks. BERT uses a masked language modeling objective, while XLNet uses a permutation language modeling objective. This experiment is based on the proposed method for XLNet and BERT, using the IMDB dataset of 50K reviews and the Rotten Tomatoes dataset. Both datasets were pre-processed using data cleaning, duplicate removal, lemmatization, and handling of chat words to improve baseline results. The results indicate that XLNet achieved the highest accuracy on both datasets. Sentiment analysis provides insights into how emotions and attitudes are expressed in movie reviews, which can be used to predict a movie's performance based on its overall sentiment. Keywords: XLNet, BERT, Sentiment analysis, IMDB dataset, Rotten Tomatoes dataset, Machine learning.