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

Text Classification can be achieved through three main approaches: Rule-based approaches. These approaches make use of handcrafted linguistic rules to classify text. ... Machine learning approaches. We can use machine learning to train models on large sets of text data to predict categories of new text. ... Hybrid approaches.

Following are the steps required to create a text classification model in Python: Importing Libraries. Importing The dataset. Text Preprocessing. Converting Text to Numbers. Training and Test Sets. Training Text Classification Model and Predicting Sentiment. Evaluating The Model. Saving and Loading the Model.

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.

Text Classification is the process of categorizing text into one or more different classes to organize, structure, and filter into any parameter. For example, text classification is used in legal documents, medical studies and files, or as simple as product reviews.

A classification of chart types Comparison: comparing and sorting data points; Composition: part-to-whole comparisons; Distribution: comparison of data points along an axis; Relationship: relationship patterns between two or more variables; Evolution: time patterns; Profiling: pattern comparison.

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.

How to Build A Text Classifier with Machine Learning Choose A Model Type. Go to MonkeyLearn's dashboard and click on 'create model'. ... Select The Classification Type. Now, you'll see different classification options. ... Upload Your Data. ... Train Your Model. ... Test Your Model. ... Analyze Your Data.

An example of classifying is assigning plants or animals into a kingdom and species. An example of classifying is designating some papers as "Secret" or "Confidential." That serves to classify. Present participle of classify.

What is Text Classification? Text classification is a machine learning technique that assigns a set of predefined categories to open-ended text. Text classifiers can be used to organize, structure, and categorize pretty much any kind of text from documents, medical studies and files, and all over the web.