Movie review prediction using LSTM Algorithm

In recent years, the movie business has relied more and more on consumer input to inform choices about production, marketing, and distribution. The growing volume of user-generated content on websites like IMDb and Rotten Tomatoes has rendered manual examination of movie reviews unfeasible. This study's primary instrument for automating sentiment analysis of movie reviews is the Long Short-Term Memory (LSTM) algorithm. Word context recognition and sequential data processing are two areas where LSTM-type recurrent neural networks (RNNs) excel. This study predicts whether a movie review is positive, negative, or neutral using LSTM. The model is trained on labeled review data, which allows it to detect nuanced emotions in the text.The results demonstrate that LSTM can accurately and efficiently categorize emotion, giving film studios and producers useful data. This automated approach enhances decision-making in the film business and is a practical use of machine learning technology.