DefinePK

Abstract

In today's digital age, people can express themselves virtually through social media. In reality, businesses can't afford to ignore social feedback if they want to expand organically. Sharing one's feelings on social media also has the power to sway the opinions of others. Businesses from many walks of life have taken notice of social media because of this feature. One arena where public opinion has the potential to alter the course of political parties is politics. With over 500 million tweets sent daily and 330 million users, Twitter is a prominent microblogging platform that reflects people's political beliefs. Many different feelings are expressed in Twitter's User Generated Content (UGC). To find political attitudes and forecast party popularity, we presented a deep learning framework called the Political Opinion Mining Framework (POMF) in this article. Not only does the framework optimise training, but it also incorporates an innovative and thorough feature selection approach. To improve training quality, it uses Natural Language Processing (NLP) to gather non-linguistic contextual information. The main technique, called Deep Political Sentiment Discovery (DPSD), uses a refined version of a Recurrent Neural Network (RNN) that relies on Long Short-Term Memory (LSTM). Several cutting-edge machine learning algorithms are tested to see how the suggested framework performs. The DPSD proves to be more effective than conventional machine learning methods, according to the empirical investigation.