Sentiment Analysis of Movie Review using Hybrid Optimization with Convolutional Neural Network in English Language

There seems to be a growing amount of user-generated material online as more people become familiar with the Internet. Understanding hidden thoughts, emotions, and attitudes in tweets, emails, comments, and reviews is difficult yet essential for market analysis, brand tracking, social media tracking, and customer support. Sentiment Analysis (SA) identifies the emotional undertone of a string of words and also might basically be employed to comprehend a user's attitude, thoughts, and emotions. The Harris Hawks Optimization - Sparrow S earch Algorithm with Convolutional Neural Network i.e., (HH-SSA-CNN) proposed in this study is an innovative SA algorithm. Pre-processing, sentiment categorization, and feature extraction make up the procedure. The preprocessing phase removes the unwanted info from input text evaluations using NLP algorithms. A hybrid technique that combines review-related features and aspect-related features has been presented for efficiently retrieving the features. This method creates unique composite features for every review. The created HH- SSA-CNN is used to accomplish sentiment categorization. This approach has been used in the IMDb dataset. To assess the model's efficacy, the outcomes of the HH-SSA-CNN model are contrasted with those of alternative methodologies. The result indicates that the developed model accurately classifies the sentiments while compared to other existing methods.