An Application of Machine Learning and Artificial Intelligence in the Emotional Arc of Motion Pictures to Drive Product and Service Innovation in the Entertainment Industry

Abstract

The entertainment industry faces challenges in effectively recognizing and understanding the emotional responses of its audience. The intricacy and fundamental qualities of human emotions can often elude AI and ML models, resulting in potential misinterpretations. This study primarily focuses on developing a novel technique to analyzing the emotional journey in movies using powerful AI and ML models. The study's major goal is to create a new methodology to better understand and analyze individual movie preferences based on Emotion Intensity research, thereby improving emotional comprehension within the entertainment industry. Our methodology presents a novel way to segment scripts and uses a lexicon-based approach, allowing the proposed work to benefit from a manually curated lexicon of terms associated with emotions. NRCLex which has been proven for providing emotion analysis has been adopted. The Lexicon categorizes text into eight basic emotions: anger, fear, anticipation, trust, surprise, sadness, joy, disgust, and two sentiments: positive and negative. We used the emotional intensity of each subscript to process it through combination of clustering methods, including K-means, Gaussian distribution, and Minibatch Kmeans. Cluster are created using custom methodology to create a pattern and then associated these patterns with emotional arcs. After analyzing various screenplays, we discovered six distinct emotional trajectories: Rags to Riches, Riches to Rags, Man in a Hole, Icarus, Cinderella, and Oedipus. We also looked at the emotional trajectory of how emotions move through a script, including positive emotional intensity (trust, anticipation, joy, surprise) and negative emotional intensity (anger, fear, sadness, and disgust). This research is essential to businesses, particularly in the entertainment industry, as it provides a prospective decision support system that could greatly enhance the scriptwriting process. By evaluating their scripts for emotional arcs, screenwriters can craft narratives that resonate more deeply with audiences, potentially leading to greater viewer engagement and satisfaction. Moreover, our findings highlight the commercial success associated with the 'Man in a Hole' emotional trajectory. This insight could influence strategic decisions in script selection, development, and production, optimizing resources towards content that has a higher likelihood of success. In turn, this could lead to improved financial performance and market competitiveness for businesses in the entertainment sector.