TOXIC COMMENTS CLASSIFICATION BASED ON DEEP LEARNING

Nowadays so many people using social media for express the feelings like uplode the photos and video on social media and this photos and videos give some comments this comments like good or toxic, Social networks sometimes become a place for threats, insults and other components of cyber bullying. A huge number of people are involved in online social networks. Hence, the protection of network users from anti-social behavior is an important activity,but some people give harassment, The harassment physically or direct is controlled by police and other forces but online harassment should be controlled by some models that restrict the user not to post a comment by identifying the comment toxicity level. One of the major tasks of such activity is automated detecting the toxic comments. Toxic comments are textual comments with threats, obscene, racism etc. To prevent this we come up with a solution, in that various techniques are used for human-free detecting the toxic comments. Bag of words statics and bag of symbols statics are the typical source of information for the toxic comments detection. Usually, the following statistics-based features are used: length of the comment, number of tokens with non-alphabet symbols, number of abusive, arguments. Aggressive, and threatening words in the comment, etc. A neural network model is used to classify the comments. In this paper, Kaggle’s toxic comment dataset is used to train deep learning model and classifying the comments in following categories: toxic, severe toxic, obscene, threat, insult, and identity hate