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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 datachallenging, time-consuming, and vulnerable to misinterpretation. This paperdescribes a classification model for movie reviews founded on deep learningapproaches. Almost 500KB pairs of balanced data from the IMDb movie reviewdatabases are employed to train the model. People's perspectives regardingmovies were classified using both the long short-term memory (LSTM) andconvolutional neural network (CNN) strategies. According to the findings, theCNN 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 ofmodel parameters. According to the outcomes, LTSM outperforms CNN in assessingIMDb movie reviews and is computationally less costly than LSTM.

DL based analysis of movie reviews