Fılm Serıes Revıew Analysıs Usıng Twıtter Apı And Text Mınıng Model
Keywords:
Film Series, Twitter, Machine Learning, Naive BayesAbstract
The development of information technology has brought about many changes to society in the industrial, economic, social, and even digital realms, including in the world of filmmaking. As the world of filmmaking has evolved, genres within film series have proliferated. This has created difficulties in navigating through recommendations for currently airing series due to the lengthy duration of episodes. Consequently, every enthusiast intending to watch a series must possess a high level of attraction to follow each episode. With the abundance of reviews, this can serve as a consideration for every viewer regarding a film series, allowing conclusions to be drawn about it. This research utilizes data from Twitter processed through machine learning and calculated using algorithms, comparing the results of analysis and experiments that demonstrate fairly accurate accuracy. The above research findings prove that the use of the Naïve Bayes machine learning algorithm provides a comprehensive overview of the assessment of film series. The predictions from the Naïve Bayes algorithm demonstrate sufficiently accurate accuracy compared to the experimental method and the analysis results from Rotten Tomatoes.
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