Child Tracking and Prediction of Violence on Children In Social Media Using Natural Language Processing and Machine Learning

Crimes against children are a direct threat to the world of tomorrow. The future of the world is in the hands of children who are the real wealth of mankind. The challenge for society is to address and handle challenges against children such as sexual abuse, trafficking, child labour, and harassment. Even though measures are taken by several governments globally, violence against children is an evident day-to-day process. This becomes the most challenging issue in the post-Internet era, where organized crime against children takes place around the world through a targeted group of people through virtual private networks and the dark web. What comes next is the emergence of social media, which provides an opportunity for these criminals to harm children globally through powerful social networks [15]. The existing methods focus on classifiers that do not work across multiple keywords [6] to look for sentiment analysis regarding a single classifier. There are only a few works that address the issue and these methods do not employ the multi-classification model [2]. In this paper, we propose a model which includes the Twitter API, the APIFY web scraper framework, as well as the VADER semantic analyzer in Python which possesses Decision tree analysis of sentiment prediction. The proposed work provides 99.6904% accuracy and predicts the classification of sentiment analysis. Our work has novelty in attribute selection, child trade, and violence analysis in social media and prediction of classification accuracy using the Decision Tree algorithm.