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Aspect based sentiment analysis (ABSA) is a methodology used in various fields for analyzing the reviews of customers to project an overall opinion on any different product or service. With the evolution of the internet, people are provided with a time saving cheap procedure like this, to express viewpoints to a mass audience. With the intension of gaining market value, numerous fields are bestowed the chance in collecting information from the internet, free of charge. The application of Machine Learning (ML) methods for the procedure of evaluation regarding aspects connected to TV series and movies, has not been initiated. This would be a novel progression for the film industry. Research under this study focuses on implementing ABSA on TV series or movies based on sub aspects of genre as well as sub aspects of cast and crew. As the initial step, web scraping can be used for data collection from social media. Next, data is processed to generate adequate results for anyone who needs a broader understanding on significance of a TV series or a movie related to the aspects mentioned earlier. To collect information belonging to each aspect is further analyzed. The accuracy of the results of the suggested system has been gained high as 79% and above. In conclusion, the accuracy of the solution is greatly successful compared to the previous tasks with high market value.
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