CNN-based Deep Learning Techniques for Movie Review Analysis of Sentiments

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Twitter, Facebook, Instagram, etc. are just a few of the many online discussion platforms that have sprung up as a result of the explosion in internet use and popularity, giving individuals a place to air their views on current events. Films get both acclamation and criticism from the general public. As a major form of entertainment, they inspire user evaluations of film and television on websites like IMDB and Amazon. Scientists and researchers give careful thought to these critiques and comments in order to extract useful information from the data. This data lacks organisation but is of critical importance nevertheless. Opinion mining, also known as sentiment classification, is a growing field that uses machine learning and deep learning to analyse the polarity of the feelings expressed in a review. Since text typically carries rich semantics useful for analysis, sentiment analysis has grown into the most active investigation in NLP (natural language processing). The continuous progress of deep learning has substantially increased the capacity to analyse this content. Convolutional Neural Networks (CNN) are commonly utilised for natural language processing since they are one of the most successful deep learning methodologies. This paper elaborates on the methods, datasets, outcomes, and limits of CNN-based sentiment analysis of film critics' reviews.