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Let's explore some of the most widely-used algorithms for text classification: Naive Bayes. ... Support Vector Machines. ... Deep Learning. ... Metrics and Evaluation. ... Supervised Machine Learning. ... Unsupervised Machine Learning. ... Semi-Supervised Machine Learning.

Some Examples of Text Classification: Sentiment Analysis. Language Detection. Fraud Profanity & Online Abuse Detection.

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

Text classification makes it possible to assign predefined categories to a document and organize it to help you find the information you need or simplify some activities. For example, an application of text categorization is spam filtering in email.

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.

Top 5 Classification Algorithms in Machine Learning Logistic Regression. Naive Bayes. K-Nearest Neighbors. Decision Tree. Support Vector Machines.

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.

Python is the most popular language when it comes to text classification with Machine Learning. Python text classification has a simple syntax and several open-source libraries available to create your algorithms.

Text Classification Workflow Step 1: Gather Data. Step 2: Explore Your Data. Step 2.5: Choose a Model* Step 3: Prepare Your Data. Step 4: Build, Train, and Evaluate Your Model. Step 5: Tune Hyperparameters. Step 6: Deploy Your Model.

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.

The 1D CNN has been proven to work well on text classification despite only a little parameter tuning [10]. On the other hand, BiGRU works well on temporal data by taking both earlier and later information in the sequence.