Sentiment analysis has become an important aspect of natural language processing, particularly in evaluating public opinions and sentiments expressed in textual data. Reviews are a good source for critics and casual viewers to express how they feel about the movie. This research paper presents a comprehensive comparative study between Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) networks for sentiment analysis of movie reviews. Our main aim is to explore the strengths and weaknesses of these two approaches in capturing the nuanced sentiments embedded in movie-related text. Our study employs a diverse and well-curated dataset which consists of fifty-thousand movie reviews on IMDB, encompassing a wide range of genres and sentiments. The dataset is well balanced. The preprocessing involves techniques such as tokenization, stemming, and vectorization to ensure the models’ effective comprehension of the semantic context.