Real Time Data Twitter Trends Polling Using Rae Model - K. S., Jyothi; C. P., Shantala (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Social media holds valuable insights into individuals and society, offering a wealth of data to propel research across various domains, like business, finance, health, socio-economic inequality, and gender vulnerability. Within this landscape, Twitter emerges as a prominent platform primarily utilized for emotive expression around specific events. Functioning as a micro-blogging hub, Twitter serves as a conduit for gathering opinions on products, trends, and political discourse. Twitter generates an immense volume of data, contributing significantly to the challenges associated with big data. Among these challenges lies the complexity in classifying tweets, stemming from the intricate and sophisticated language used, rendering existing tools inadequate. Despite extensive efforts dedicated to this issue, there remains a lack of definitive validation aligning online social media trends with conventional survey results. Sentiment analysis emerges as a method aimed at scrutinizing the sentiments, emotions, and viewpoints of diverse individuals regarding various subjects, capable of examining public opinion expressed in tweets related to news, policies, social movements, and influential figures. Sentiment Analysis has leveraged Machine Learning Classifiers, enabling the automation of opinion mining without the need for manual tweet reading. Machine Learning models have consistently demonstrated impressive outcomes across diverse applications. Thus, this study introduces the utilization of the Real-time Advanced Ensemble Learning (RAE) model for live Twitter trend polling based on real-time data. The effectiveness of this approach will be assessed through metrics such as training and validation accuracy, as well as training and validation loss. Expectations suggest that this model will yield notable advancements compared to previous methods.