Decoding Sentiment: A CNN Approach To Sign Language Interpretation For Movie Review

Objectives:We propose a unique approach to analyze the hand gesture sentiments of the elderly or the deaf and dumb using a machine learning algorithm.Methods: We created a categorical collection of hand gesture photographs classified by age and gender.When creating models with the CNN architecture, accuracy is determined using training and testing data.Sentimental text is generated from sign language and classified using classifiers.Sentiments are categorized as 'Very Nice', 'Nice', and 'Bad'.Findings: We provide a novel approach to sentiment analysis based on sign language, the first of its kind in the literature, for movie evaluations made by deaf and/or hearing-impaired people utilizing hand gestures.Using our own dataset, we developed and enhanced custom models for extracting words from hand gesture expressions.Novelty: Sentiment analysis is focused on identifying hand gesture signs used by the Deaf and Dumb community, the elderly, or the disabled, which use three different signs such as Very nice, Nice, and Bad.