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Natural Language Processing techniques have gained popularity for the analysis of social media content. A number of techniques have been proposed to analyze various aspects such as public opinion, sentiments, and emotions expressed, opinion leaders, and extreme views. However, existing approaches take a retrospective approach that studies opinions after the occurrence of events. With the buildup of negative sentiments and extreme public opinion potentially leading to violent actions and civil disobedience, there is a need for a proactive and predictive approach that can offer early warning signs to government officials to intervene. In this work, we propose such an approach by combining two natural language processing techniques: Named entity recognition (NER) and emotions analysis. By tagging important entities within posts, such as prominent figures and important locations, and analyzing whether …