Decoding Animation Audiences: Exploring Customer Reviews and Sentiments in Animation Films

Animation movies represent a distinct form of cinematic expression, characterized by the manipulation of images to create dynamic movement and vivid storytelling. Unlike live-action films, animation movies leverage digital or traditional techniques to bring characters and environments to life, offering a wide spectrum of creative possibilities. This paper explores the cultural significance and rising popularity of animation movies, emphasizing their ability to appeal to diverse audiences and tackle complex themes in imaginative ways. It outlines the objectives of a study aimed at analyzing customer reviews of animation movies, exploring underlying sentiments, and employing machine learning, specifically the random forest algorithm, for sentiment classification. Utilizing a comprehensive dataset of 52,000 Animation Movie Details sourced from the TMDB API, the study employs a classification model based on the Random Forest algorithm to extract insights from the data. Despite encountering computational challenges, the Random Forest model demonstrates promising potential for enhancing customer feedback analysis and market sentiment tracking in the realm of animation movies.