Classify comment log easily

Aug 6th, 2022
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How to classify comment log

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The video tutorial explores how to detect toxic comments online using deep learning. It will focus on identifying different elements of toxicity such as severe toxicity, basic toxicity, and threats within sentences. The tutorial will cover loading data, preprocessing information, and tokenization techniques to analyze and detect toxic language more effectively.

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Toxic comment classification is a popular kaggle competition in the field of nlp. The competition has ended around two years ago. The main objective of the challenge was to find different types of toxicity of like threats, obscenity, insults, and identity-based hate on online comments.
By working with different types of Neural Network models with word embedding initializations, we could conclude which models may be better suited for the task of toxic comment classification. We found that the best model in our case was the CNN model with word embedding.
The six detectable types are toxic, severe toxic, obscene, threat, insult, and identity hate.
Toxic comment classification is a popular kaggle competition in the field of nlp. The competition has ended around two years ago. The main objective of the challenge was to find different types of toxicity of like threats, obscenity, insults, and identity-based hate on online comments.
Toxic comment classification is a popular kaggle competition in the field of nlp. The competition has ended around two years ago. The main objective of the challenge was to find different types of toxicity of like threats, obscenity, insults, and identity-based hate on online comments.
Conclusion: A deep learning model to classify toxic comments is built with accuracy of 98.8%.
Toxic comments are textual comments with threats, insults, obscene, racism etc. The various techniques are used for human-free detecting the toxic comments. Bag of words statistics and bag of symbols statistics are typical source information for the toxic comments detection.
Toxic comments are textual comments with threats, insults, obscene, racism etc. The various techniques are used for human-free detecting the toxic comments. Bag of words statistics and bag of symbols statistics are typical source information for the toxic comments detection.
Conclusion: A deep learning model to classify toxic comments is built with accuracy of 98.8%.
There are six types of toxicities in this data: toxic, severe-toxic, obscene, threat, insult and identity-hate. A comment may fall under more than one category. As a result, it becomes a multilabel classification problem.

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