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Machine Learning is a technique that aims to learn from data over a task based on certain performance measures. With the flooding of data and increased computational ability, machine learning algorithms have a huge research scope. The task of identifying the correct machine learning algorithm for an application is very challenging without analysing the basic information about the problem such as the domain of the undertaken problem, the features available in the data, etc. The movie industry, a huge part of entertainment industry, has seen a phenomenal growth throughout the globe in recent times. A movie can capture the attention of a viewer and can trigger cognitive and emotional processes in the brain. In this research, the emotional outcome of the viewer was analysed while they watch the movie before its actual release that is, during its preview. Traditionally Functional Magnetic Resonance Imaging device was used to assess human brain activity but proved to be non-feasible and costly so EEG sensors were used to monitor and record the functioning of the brain of volunteers for further analysis. We proposed a model to use the collected data through EEG sensors were analysed using artificial neural network which was Used to find high and low of different brain waves mapping to the emotions depicted in every scene of the movie. Performance measures such as accuracy, precision and recall were calculated to validate our proposed model. Our proposed model resulted in providing assistance to movie makers who could Study the pulse of audience before the actual release and could incorporate changes if necessary.