EMPOLITICON: NLP and ML Based Approach for Context and Emotion Classification of Political Speeches From Transcripts

Abstract

Political speeches have played one of the most influential roles in shaping the world. Speeches of the written variety have been etched into history. These sorts of speeches have a great effect on the general people and their actions in the coming few days. Moreover, if left unchecked, political personnel or parties may cause major problems. In many cases, there may be a warning sign that the government needs to change its policies and also listen to the people. Understanding the emotion and context of a political speech is important, as they can be early indicators or warning signs for impending international crises, alignments, wars and future conflicts. In our research, we have focused on the presidents/prime ministers of China, Russia, the United Kingdom and the United States which are the permanent members of the United Nations Security Council and classified the speeches given by them based on the context and emotion of the speeches. The speeches were categorized into optimism, neutral, joy or upset in terms of emotion and five context categories, which are international affairs, nationalism, development, extremism and others. Here, optimism is a secondary emotion, whereas joy and upset are primary emotions. Apart from classifying the speeches based on context and emotion, one of the major works of our research is that we are introducing a dataset of political speeches that contains 2010 speeches labelled with emotion and context of the speech. The speeches we have worked on are large in word count. We propose EMPOLITICON-Context, a soft voting classifier ensemble learning model for context classification and EMPOLITICON-Emotion, a soft voting classifier ensemble learning model for emotion classification of political speeches. The proposed EMPOLITICON-Context model has achieved 73.13% accuracy in terms of context classification and the EMPOLITICON-Emotion model has achieved 53.07% accuracy in classifying the emotion of the political speeches.