Implementation of Natural Language Processing for Bullying Complaints with Voice to Text Conversion
Abstract
Bullying in high school is frequent and negatively affects students' psychological well-being. The lack of effective reporting mechanisms makes students hesitant to report bullying cases for fear of their identity being exposed, which can lead to stigma or retaliation. The lack of data on bullying incidents also hampers prevention and intervention measures. The study designed and implemented a mobile application that uses natural language processing (NLP) for speech-to-text conversion, enabling anonymous and convenient reporting of bullying cases. The app ensures the anonymity of whistleblowers and facilitates the collection of accurate data on bullying incidents, helping schools respond with appropriate preventive measures. Software engineering methodologies are used with a focus on requirements analysis, system design, implementation, and application testing. NLP technology is used to interpret verbal instructions into text, with the Support Vector Machine (SVM) method for text classification, ensuring high accuracy in detecting bullying incidents. The trial application in several high schools showed the relevance and effectiveness of the application. Ethical and security considerations are top priorities, with an emphasis on whistleblower identity protection and data security. The test results showed that the application achieved 92% accuracy, 90% precision, and 88% recall, demonstrating its effectiveness in collecting bullying reports anonymously and accurately.
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References
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