click here to download project abstract of cyber bullying
click here to download project abstract of cyber bullying
At DataPro, we provide final year projects with source code in python for computer science students in Hyderabad , Visakhapatnam.
ABSTRACT
Introduction: In the digital age, the prevalence of cyberbullying has become a critical concern, necessitating advanced detection methods to safeguard individuals from online harassment. so This abstract explores an innovative approach to cyberbullying detection, emphasizing the significance of proactive measures in addressing this growing societal issue.
Methodology: Our research employs a multifaceted methodology integrating natural language processing (NLP) and machine learning algorithms.
Key Features: The system incorporates a range of linguistic and behavioral features, including sentiment analysis, contextual understanding, and identification of offensive language. By employing active voice and dynamic algorithms, the model adapts to evolving cyberbullying tactics, ensuring robust and real-time detection capabilities.
Technological Advancements: This research showcases the integration of state-of-the-art technologies, such as deep learning and neural networks, to enhance the precision and efficiency of cyberbullying detection. The active voice implementation within the algorithm allows for swift and accurate identification, keeping pace with the ever-changing landscape of online interactions.
User Interface and Accessibility: so To maximize impact, the developed system boasts a user-friendly interface, providing accessible and actionable insights for users and moderators. The integration of transition words facilitates smooth communication of results, ensuring a seamless experience for those utilizing the detection tool.
Results and Implications: Preliminary results demonstrate a significant improvement in cyberbullying detection rates compared to existing methods.
Conclusion: In conclusion, this research presents a comprehensive and technologically advanced approach to cyberbullying detection.
