There are always discussions regarding police actions, particularly the excessive use of force and hence severe public response. This study dives into a detailed exploration aiming to decode the complex patterns and viewpoints found within textual data linked to these critical matters. This paper attends to do so by leveraging a solid text mining strategy. Using a dataset that includes narratives from both police reports and public complaints, this investigation adopts sophisticated Natural Language Processing (NLP) to unearth valuable insights. It uncovers the underlying public sentiments and pinpoints prevalent themes in the discussions related to incidents of police using force. The research delves into various aspects of the narratives and provides a detailed insight into the outcomes of police actions from a data-centric perspective. The research carefully breaks down public complaints and performs topic modelling to expose the apprehensions, anxieties, and other public views. The outcomes of this study not only offer a thorough understanding of the dominant narratives in police actions and public responses but also create a pathway for enlightened policy development. By intersecting technology and societal matters, this paper highlights text mining’s potential to provide a nuanced, impartial, and deep analysis of these issues.