Abstract

The pervasive influence of social media has reshaped public communication, particularly for institutions like law enforcement agencies that rely on these platforms to engage with the community. As police departments increasingly utilize social media to disseminate information, interact with the public, and manage public relations, the need for effective sentiment analysis becomes crucial. Understanding how police messages are received by the public, especially by the youth, who are among the most active and impressionable users of social media, is essential for maintaining public trust and ensuring effective communication. This thesis, seeks to develop a comprehensive solution for this challenge. The primary objective of the research is to create a robust sentiment analysis model that can accurately classify the sentiments expressed in social media posts by the police, with a particular focus on fine-grained sentiment classification. This involves not only identifying whether the sentiment is positive, negative, or neutral but also capturing the nuanced emotions and opinions that can significantly influence public perception. To achieve this, the thesis proposes a hybrid approach that combines traditional rule-based methods with advanced machine learning and deep learning techniques. Rule-based methods offer the advantage of domain-specific knowledge, allowing for precise identification of sentiment-indicative phrases and words, while machine learning models, particularly those based on deep learning architectures, provide the ability to learn complex patterns from large datasets. By integrating these approaches, the proposed model aims to achieve higher accuracy and reliability in sentiment classification, even in the context of the highly varied and context-dependent language used in social media. The research also emphasizes the importance of real-time sentiment analysis. Given the rapid pace at which information spreads on social media, especially during critical incidents, a system that can monitor and analyze sentiment in real-time is crucial for law enforcement agencies. Such a system can provide immediate feedback, enabling the police to respond promptly to emerging public concerns or misinformation, thereby mitigating potential negative impacts, particularly on youth who are highly susceptible to the influence of online content. This thesis represents a significant step forward in the application of sentiment analysis to public sector communication, offering a novel approach to managing the complexities of big data in social media and addressing the critical issue of its impact on youth.

Library of Congress Subject Headings

Sentiment analysis; Police in mass media; Social media in government; Big data; Police--Public opinion; Youth--Attitudes

Publication Date

5-20-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Ehsan Warriach

Campus

RIT Dubai

Plan Codes

PROFST-MS

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