Mining population opinion about local police

Abstract

Sentiment analysis, or opinion mining, is an important task of natural language processing (NLP) that extracts opinions, attitudes, and emotions from text. With the growth of digital platforms like blogs and social networks, opinion mining has become a key tool for organizations to understand public sentiment. In recent research, machine learning and lexicon-based approaches have been applied to analyze sentiments. Our work specifically focuses on national security, where sentiment analysis offers crucial insights into local opinions, helping authorities gauge public mood. As part of our research, we developed the Public Sensing about Police Platform, a prototype system designed to analyze emotions from social networks. This system generates dashboards for law enforcement and security agencies, providing actionable intelligence for public safety. Our findings show that “Hate” was the most common emotion expressed in relation to police interventions, indicating widespread unpopularity of these actions and a resulting sense of insecurity among the public.