Classify comment article easily

Aug 6th, 2022
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How to classify comment article

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the web can be a pretty sketchy place sometimes comments can get pretty toxic well in this video were going to take a look at exactly how to detect them using deep learning lets do it [Music] [Music] whats happening guys my name is nicholas fronot and in this video were going to be taking a look at common toxicity so well be able to take some sentences of natural language pass them through a deep learning model and detect the different elements of toxicity within them this might be things like severe toxicity basic toxicity if there is such a thing as basic toxicity whether or not theres threats within those sentences so on and so forth well be able to see this in more detail as we take a look at our data now in this tutorial were going to be covering a bunch of stuff but specifically were first im going to focus on loading in our data were then going to take a look at what we need to do to pre-process that information so well specifically take a look at tokenization well

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Three tiers are created based on the impact factor of the journal: Tier 1 with 2 categories (High: top 25 percentile, and Low: 25-75 percentile), Tier 2 with two categories (High: lower 25 percentile of journals with impact factor and Low: Peer-reviewed Indexed journals without impact factor), and Tier 3 to capture all
Journals are also categorised into four quartiles: Q1, Q2, Q3 and Q4. Journals in Q1 and Q2 are considered top-tier journals while the lower-tier journals are classified as Q3 and Q4. Q1 and Q2 journals have a higher impact factor than Q3 and Q4 ranked journals.
The text types are broken into three genres: Narrative, Non- fiction and poetry. Each of these genres has then been sub-divided into specific text types such as adventure, explanation or a specific form of poetry, e.g. haiku. Narrative is central to childrens learning.
Text classification also known as text tagging or text categorization is the process of categorizing text into organized groups. By using Natural Language Processing (NLP), text classifiers can automatically analyze text and then assign a set of pre-defined tags or categories based on its content.
Some Examples of Text Classification: Sentiment Analysis. Language Detection. Fraud Profanity Online Abuse Detection.
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.
Text classification also known as text tagging or text categorization is the process of categorizing text into organized groups. By using Natural Language Processing (NLP), text classifiers can automatically analyze text and then assign a set of pre-defined tags or categories based on its content.
Some Examples of Text Classification: Sentiment Analysis. Language Detection. Fraud Profanity Online Abuse Detection.
Identifying Scholarly Articles Author(s) name included. Technical or specialized language. Written for professionals. Charts, graphs, and diagrams. Long ( 5 or more pages) Bibliography included.
More formally, text classification is an analytical process that takes any text document as input and assigns a label (or classification) from a predetermined set of class labels.

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