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Parameters for data categorization Based on value. Based on usefulness timeframe. Based on information type. Based on who it pertains to – clients, employees, etc. Based on requirements to refresh. Based on retrieval rights. Based on storage location/ device.

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

Rule-based approaches classify text into organized groups by using a set of handcrafted linguistic rules. These rules instruct the system to use semantically relevant elements of a text to identify relevant categories based on its content. Each rule consists of an antecedent or pattern and a predicted category.

The scope of text classification is at document level, paragraph level, sentence level, or even sub-sentence level. In some applications we may assign one or more classes to an entire document. In other applications, we may assign a class to each paragraph or sentence in the document.

A. An example of text classification is classifying emails as spam or not spam. Another is sentiment analysis, where texts are categorized as positive, negative, or neutral.

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.

Linear Support Vector Machine is widely regarded as one of the best text classification algorithms.

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.