Encapsulating Abstract Consequences of the Pandemic on the Film Industry through Neural Networks via Mixed Methods Analysis.
Submitted to Journal of Mixed Methods Research.
The field of mixed methods can benefit from utilizing various Deep Learning and Machine Learning informed techniques to encapsulate abstract concepts into quantifiable data. This article shows how the ordinalization of different aspects of movies through Neural Networks can help investigate the cause of the revenue crash of film studios with excellent track records after the Covid-19 pandemic. Mixed methodology is enforced by considering the Video, Audio, Script, and other metadata pertaining to a movie. Apart from the aforementioned holistic case study, each aspect of the movie is also individually evaluated to highlight the settings of a successful movie. These examinations revealed subconscious biases that most successful movies exploit to generate high revenue. This research also utilizes the extensive work done on analyzing movie reviews to create a statistical basis for proving the efficacy of the proposed neural network.
Mixed methods, Deep learning, Big Data, Movie, Supervised learning