Classify Comment Bulletin For Free

The system is designed to detect and classify toxic comments or texts while chatting. It helps people refrain from using negative or profane language while interacting with others and promote healthy conversation among users. The system comprises 1 module: User.

Sentiment Classification Loading The dataset. Exploring Dataset. Text Pre-Processing. Build a model for sentiment classification. Split dataset. Make the prediction on test case. Finding model Accuracy.

Some Examples of Text Classification: Sentiment Analysis. Language Detection. Fraud Profanity & Online Abuse Detection. Detecting Trends in Customer Feedback. Urgency Detection in Customer Support.

For each id in the test set, you must predict a probability for each of the six possible types of comment toxicity (toxic, severe_toxic, obscene, threat, insult, identity_hate).

As a multi-label classification problem, here a comment can be classified to have no label or one or more than one labels. The six toxic labels presented in the data are ?Toxic?, ?Sever Toxic?, ?Obscene?, ?Threat?, ?Insult?, and ?Identity?.

The data consists of one input feature, the string data for the comments, and six labels for different categories of toxic comments: toxic, severe_toxic, obscene, threat, insult, and identity_hate.

The six detectable types are toxic, severe toxic, obscene, threat, insult, and identity hate.

How To Categorize User Comments? NLP. Sentiment Analysis. Text Classification. Topic Tagging. Word Association.

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 are: Understanding audience sentiment from social media, Detection of spam and non-spam emails, Auto tagging of customer queries, and.