  Undoubtedly, social media are brainstormed by a tremendous volume of stories,
feedback, reviews, and reactions expressed in various languages and idioms,
even though some are factually incorrect. These motifs make assessing such data
challenging, time-consuming, and vulnerable to misinterpretation. This paper
describes a classification model for movie reviews founded on deep learning
approaches. Almost 500KB pairs of balanced data from the IMDb movie review
databases are employed to train the model. People's perspectives regarding
movies were classified using both the long short-term memory (LSTM) and
convolutional neural network (CNN) strategies. According to the findings, the
CNN algorithm's prediction accuracy rate would be almost 97.4%. Furthermore,
the model trained by LSTM resulted in accuracies of around and applying 99.2%
within the Keras library. The model is investigated more by modification of
model parameters. According to the outcomes, LTSM outperforms CNN in assessing
IMDb movie reviews and is computationally less costly than LSTM.
