Classify Text For Free

Text categorization (a.k.a. text classification) is the task of assigning predefined categories to free-text documents. It can provide conceptual views of document collections and has important applications in the real world.

Text classification is the process of classifying documents into predefined categories based on their content. It is the automated assignment of natural language texts to predefined categories.Existing supervised learning algorithms for classifying text need sufficient documents to learn accurately.

I have 4,500 categorized documents with 17 categories, and I used ration for training and test dataset. I used Sklearn python library. The best classification accuracy I have managed to get is 61% and I need it to be at least 85%.

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.

Text classification is a supervised learning problem, which categorizes text/tokens into the organized groups, with the help of Machine Learning & Natural Language Processing.

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.

The two main deep learning architectures for text classification are Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The answer by Chiranjibi Sitaula is the most accurate. If the order of works matters then RNN and LSTM should be best.