Published in: Journal of Computer Science and Engineering in Innovations and Research (ISSN No: 3049-1762 online)
Publication Date: June 15, 2025
Peoples are engaged social network now a days. They are posted different comments on different topics.There are different approach to analysis them. This research works develops a software system using sentiment analysis to detect and monitor drug-related discussions on digital platforms. By analyzing tone, intent, and context, it provides an automated tool for early detection, aiming to combat drug abuse and trafficking through innovative technology.
Sentiment analysis, Machine Learning, Data Flow Diagram
The internet has become a platform for both valuable information exchange and illegal drug-related discussions. This project aims to develop a software system using sentiment analysis to detect and analyze drug-related content online, distinguishing between illegal trafficking, abuse, and medicinal use to enable targeted interventions. The system provides a scalable solution to digital drug proliferation by automating sentiment analysis, contributing to software engineering and public health. It showcases technology's role in enhancing safety and combating drug abuse.
The world is increasingly immersed in social media, where individuals engage in discussions on diverse topics and share varied opinions. This study explores the concept of homophily in social media perception analysis, examining how individuals tend to associate and interact with others who share similar views, interests, and backgrounds, and how this phenomenon influences their perceptions and interactions online [1]. Social media has become a ubiquitous platform in our society, where people freely share their opinions and sentiments. With the vast amount of user-generated content, various techniques have emerged for analyzing tweets expressed on social media. This research work explores different sentiment analysis techniques, highlighting their applications, strengths, and limitations in understanding public opinions and emotions on social media posts [2].