Abstract
Toxic comments are disrespectful, abusive, or unreasonable online comments that usually make other users leave a discussion. The danger of online bullying and harassment affects the free flow of thoughts by restricting the dissenting opinions of people. Sites struggle to promote discussions effectively, leading many communities to limit or close down user comments altogether. This paper will systematically examine the extent of online harassment and classify the content into labels to examine the toxicity as correctly as possible. Here, we will use two machine learning algorithms and apply them to our data to solve the problem of text classification and to identify the best machine learning algorithm based on our evaluation metrics for toxic comments classification. We will aim at examining the toxicity with high accuracy to limit down its adverse effects which will be an incentive for organizations to take the necessary steps
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