News Articles Classification | NLP | Text Classification | Hands-on with Python | Doc2vec | Part 4




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This video is Part 4 of 4

The goal will be to build a system that can accurately classify previously unseen news articles into the right category.

Text classification is the process of assigning tags or categories to text according to its content. It’s one of the fundamental tasks in Natural Language Processing (NLP) with broad applications such as sentiment analysis, topic labeling, spam detection, and intent detection.

We will follow a systematic approach with step by step implementation in Python Programming Language.

Code and Data: https://github.com/DiveshRKubal/Data-Science-Use-Cases/tree/master/News%20Classification

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Comment List

  • Solve Business Problems Using Analytics
    December 6, 2020

    Thank you very much sir, the tutorial is so beneficial. I want to know if the dataset of news articles is multi-label, do train_vectors_dbow and test_vectors_dbow have to be transformed? thankful if you would continue the tutorial doc2vec for multi-label classification problem

  • Solve Business Problems Using Analytics
    December 6, 2020

    Thank you for this video series. Would like to know more about word2vec, lstm etc.

  • Solve Business Problems Using Analytics
    December 6, 2020

    Thank you very much for your tutorial. Most grateful if you can apply the LSTM(Or GRU?) after Doc2Vec. Thanks alot

  • Solve Business Problems Using Analytics
    December 6, 2020

    Thanks for clear and understandable explanation of Hyper tuning parameter. I have a doubt, when we build a document bag of words model that dbow on combined X_train and X_test in all_data, will it give same accuracy on unseen data… As i seen we got pretty good accuracy on building dbow on combined data set…I also seem forward to the use cases having separate train and test data frames… again thanks a lot..

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