Hate Speech Detection using Machine Learning
Cyberbullying is a type of harassment that take place in online mode that is in troubling and unsettling. In social media cyberbullying is a common and people end up with mental breakdown state. Automated detection of cyberbullying message on majority of social media platforms is necessary with the use of intelligent system. We proposed a project based on cyber bullying detection system to address this bullying messages and address this issues. In this system, a deep learning framework that will detect messages sent by sender as well as correctly identify any cyberbullying content in them. From recent researches that shows deep neural network based approaches are more effective than conventional methods to detecting cyberbullying texts.
Research work has done in this domain using various deep learning and machine learning techniques have gave significant results on detecting and preventing cyberbully. For testing and training purposes most of the work with English data. To continue with these datasets, we first have to adopt one single classification technique, 1, or -1 classifier was used for this purpose. that will tell us if the text contains cyberbullying content or not. So our model is train on black and white area and eliminate gray possibilities.
Cyberbullying Words Identification using Machine Learning
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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