ABSTRACT
With the fast development and huge application of Cloud media, foreseeing the prominence of policy data related to COVID-19 on news media is of extraordinary importance for comprehension and overseeing trending assessments. However, the intricacy of the dissemination designs of strategy data has brought incredible challenges for foreseeing the trending issues related to COVID-19. To achieve this goal, a Cloud-based prediction model is proposed. The Facebook and INvideos database related to COVID-19 are used as inputs. The attributes of policy data are drawing from three measurements: social data, textual data, and contextual data. Effective attributes, like the theme distribution and hot data significance, are recognized by verifiable analysis. The effective attributes are input to the forecast, modeled to foresee the trending data related to COVID-19. The precise forecast outcomes could profit policy producers, permitting them to settle on better choices and understand and control people's judgments. To achieve such a functionally powerful model, the “Rapid-Miner” simulator is used and the proposed model is known as “Framework for Trending Data Prediction.” This method provides great efficiency, performance, accuracy, and this model requires less execution time to predict the data over social media networks related to COVID-19.
