首页|New Findings on Machine Learning Described by Investigators at University of Tul sa (Impact of Social Media Posts’ Characteristics On Movie Performance Prior To Release: an Explainable Machine Learning Approach)
New Findings on Machine Learning Described by Investigators at University of Tul sa (Impact of Social Media Posts’ Characteristics On Movie Performance Prior To Release: an Explainable Machine Learning Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in Machine Learning. According to news reporting originating in Tulsa, Oklahoma, by NewsRx journalists, research stated, “In the era of invasive social media and advanced artificial intelligence, sentiment analysis has become a vital tool for e-comme rce and businesses to grasp user needs and monitor brand perception. This is par ticularly relevant in the film industry, where understanding the determinants of a movie’s pre-release performance is crucial for producers and investors.” The news reporters obtained a quote from the research from the University of Tul sa, “Traditional methods often rely on complex algorithms that lack transparency in elucidating the relationship between key risk factors and movie outcomes. Th is study addresses this gap by employing an explainable analytics framework to i nvestigate the impact of various social media post characteristics on movie perf ormance before its release. Initially, an exploratory data analysis was undertak en to identify significant risk factors associated with movie failures. Subseque ntly, the study segmented the analysis into three risk categorieslow, moderate, and high risk-and applied conventional machine learning models to forecast the likelihood of failure within each category. The culmination of this research inv olved the application of a SHapley Additive exPlanation (SHAP) model, which prov ided insightful interpretations of how different risk factors contribute to the potential success or failure of movies.”
TulsaOklahomaUnited StatesNorth an d Central AmericaCyborgsEmerging TechnologiesMachine LearningRisk and Pr eventionUniversity of Tulsa