Sentiment analysis is a computational method that extracts emotional keywords from different texts through initial emotion analysis (e.g., Happy, Sad, Positive, Negative & Neutral). A recent study by a human rights organization found that 30% of children in Bangladesh are being abused on online in the COVID-19 epidemic by various obscene comments. The main goal of our research is to collect textual data from social media and classify the way children are harassed by various abusive comments online through the use of emoji in a text-mining method and to expose to society the risks that children face online. Another goal of this study is to set a precedent through a detailed study of child abuse and neglect in the big data age. To make the work effective, 3373 child abusive comments are collected manually from online (e.g. Facebook, Newspapers and various Blogs). At present, there is still a very limited number of Bengali child sentiment analysis studies. Fine-tuned general purpose language representation models, such as the BERT family model (BERT, Distil-BERT), and glove word embedding based CNN and Fast-Text models have been used to successfully complete the study. We show that Distil-BERT defeated BERT, Fast-Text, and CNN by 96.09% (relative) accuracy, while Bert, Fast-Text and CNN have 93.66%, 95.73%, and 95.05%, respectively. But observations show that the accuracy of the Distil-BERT does not differ much from the rest of the models. From our analysis, it can be said that the pre-trained models performed outstanding and in addition, child sentiment analysis can serve as a potential motivator for the government to formulate child protection policies and build child welfare systems.