DEVELOPMENT OF A CHILD ABUSE MONITORING AND REPORTING SYSTEM USING A NATURAL LANGUAGE PROCESSING MODEL

Abstract: Child abuse remains a major global concern that threatens the physical, emotional, and psychological development of children. Despite growing awareness and legal frameworks aimed at protecting children, many abuse cases remain undetected or are reported too late due to inadequate monitoring systems, poor data quality, fear of stigma, and ineffective reporting mechanisms. Psychological abuse is often difficult to detect because it does not leave visible physical evidence and is therefore frequently underreported. In addition, the rapid expansion of digital communication platforms has created new avenues through which abusive behaviors such as cyberbullying, harassment, and threats can occur, making traditional monitoring approaches insufficient. This study presents the development of the Child Abuse Monitoring and Reporting (CAMR) System that uses Natural Language Processing (NLP) to enhance the detection, monitoring, and reporting of child abuse cases. The proposed system employs sentiment analysis and Named Entity Recognition (NER) techniques to identify emotional tones and relevant entities in text that may indicate abusive interactions. The system is implemented using the Naïve Bayes algorithm to classify text and detect potentially abusive content.

The system's performance is evaluated using accuracy, precision, recall, and F1-score, along with User Acceptance Testing (UAT), to assess its effectiveness and usability. The study demonstrates that integrating NLP techniques into child protection systems can enhance early detection of abuse, enable automated monitoring of large volumes of textual data, and support timely intervention. The proposed system contributes to improving child protection strategies by providing a scalable and efficient technological solution for monitoring and reporting abuse, particularly within digital communication environments. Ultimately, the system supports government agencies, institutions, and child protection organizations in safeguarding children and responding more effectively to abuse cases.

Keywords: Child Abuse; Child Protection; Abuse Detection; Psychological Abuse; Cyberbullying; Harassment; Threats.