Maitha Ahmad

Abstract

This thesis explores fine-grained sentiment analysis in the context of police social media posts, leveraging hybrid approaches to manage the rapid flow of big data and mitigate its negative impact on youth. As social media becomes a primary medium for public interaction, understanding sentiment within police-related posts is crucial for law enforcement agencies to gauge public opinion and address community concerns effectively. The rapid dissemination of information and the potential for negative sentiments to spread swiftly pose significant challenges, particularly for young audiences. The research begins with a comprehensive review of existing sentiment analysis techniques and their applicability to large-scale social media data. The study then develops and evaluates hybrid models that combine lexicon-based methods with machine learning approaches to achieve a more nuanced understanding of sentiment in police-related posts. The proposed models are tested on a dataset of social media posts, demonstrating their ability to accurately classify sentiments while handling the complexities of big data. Results indicate that the hybrid approach not only improves sentiment classification accuracy but also effectively processes large volumes of data in real-time. The study further explores how these insights can be used to counteract the negative influence of social media on youth, proposing strategies for early detection and intervention. The findings of this research contribute to the field of sentiment analysis by offering a robust solution to the challenges posed by big data in social media. Additionally, the thesis provides practical recommendations for law enforcement agencies on how to utilize sentiment analysis to foster positive community relations and safeguard youth from harmful online content.

Library of Congress Subject Headings

Video surveillance--Data processing; Crime forecasting--Automation; Criminal behavior, Prediction of--Automation; Sentiment analysis

Publication Date

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

Ioannis Karamitsos

Campus

RIT Dubai

Plan Codes

PROFST-MS

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