NLP Tutorial 17 – Multi-Label Text Classification for Stack Overflow Tag Prediction




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Multi-Label Text Classification in Python with Scikit-Learn.
We will use the β€œStackSample:10% of Stack Overflow Q&A” dataset. It is a problem statement of a multilabel text classification algorithm. We will be developing a text classification model that analyzes a textual description of questions and predicts multiple labels associated with the question. We will implement a multilabel text classification algorithm for a tag suggestion system using Multi-Label Text Classification in Python which is a subset of multiple output models.

Text preprocessing is performed on the text data and the cleaned data is loaded for text classification. We will be implementing text Vectorization on text data, encode the tag labels using MultilabelBinarizer and model Classical classifiers(SGC classifier, MultiNomial Naive Bayes Classifier, Random Forest Classifier,…) for modeling and compare the results.

In machine learning, Classification is a type of supervised learning. classification refers to a predictive modeling problem where a class label is predicted for a given input sample. It specifies the class to which data point belongs to and is best used when the output has finite and discrete values. There are 4 types of classification tasks that you encounter, they are
1. Binary Classification
2. Multiclass Classification
3. MultiLabel Classification
4. Imbalanced classification

The algorithm’s accuracy can be increased if we use multi-label text classification using BERT or Keras multi-label text classification. You can also use multi-label text classification with XLNET and GPT-2 and GPT-3.

#NaturalLanguageProcessing #MultiLabelClassification #StackOverflowDataset
Watch till last for a detailed explanation.
00:00 Introduction
04:36 Notebook Setup
18:30 Multi-Label Binarizer
21:14 TF-IDF Vectorizer
31:02 SGDClassifier, LogisticRegression, and SVM
34:00 OneVsRestClassifier
42:48 Jaccard Similarity Score
51:16 Test the Model with Real Dataset

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Comment List

  • KGP Talkie
    January 17, 2021

    what algorithm to choose when there are more than 500 unique labels in dataset? becoz logistic regression and svm aren't working i.e not giving good jaccard score.

  • KGP Talkie
    January 17, 2021

    What are you thoughts on KNeighborsClassifier? It can be used for multilabel classification as well. Thank you for a video, its quite useful and informative.

  • KGP Talkie
    January 17, 2021

    could you please make a video on "How to extract functions/features from application description of play store?". I mean there is a description written below each app in the play store. This description comprised of some features and functions about that app. features and functions actually show what a specific app is developed for. now my question is how to extract those features from the description of the app? By using a supervised learning approach like RNN, how can we extract those features?

  • KGP Talkie
    January 17, 2021

    Where is the colab notebook link which you shared?

  • KGP Talkie
    January 17, 2021

    Thank you! This is great content. Is there a way to deal with imbalanced labels using the same methods here?

  • KGP Talkie
    January 17, 2021

    Hi thanks for the perfect video, have you uploaded any videos for extracting information's from Emil, eg extracting User name and his ID from Email.
    thanks in advance

  • KGP Talkie
    January 17, 2021

    Thankyou So much for sharing your knowledge and this code-along. Really helped.

  • KGP Talkie
    January 17, 2021

    Hi I am using your model but I have a very huge csv of data like 4lakh + records, So google colab doesn't allow it crashes after occupying up the RAM, Is there is a way to train the data into chunks and save the model after combining all the trained model.

  • KGP Talkie
    January 17, 2021

    A plesaure

  • KGP Talkie
    January 17, 2021

    Can you share the notebook link

  • KGP Talkie
    January 17, 2021

    Generally, we dont get data in right format. Could you share the preprocessing techniques you used and also notebook. Thank you.

  • KGP Talkie
    January 17, 2021

    Can you make videos on feature engineering also?

  • KGP Talkie
    January 17, 2021

    Thanks for providing such a awesome and useful content,can you make video on multilabel classification using bert?

  • KGP Talkie
    January 17, 2021

    KGP talkie, is it possible that you record a video with scraping youtube comments for one video( example: gangna style) and then create a NLP that we analyize what's people talking, thinking about it

  • KGP Talkie
    January 17, 2021

    Okay listen, you have damn awesome content. Just keep on making videos frequently and this channel is gonna have millions of followers

  • KGP Talkie
    January 17, 2021

    Great live demo. Thanks a lot !!!

  • KGP Talkie
    January 17, 2021

    Hey kindly share your email id to reach you out for a project.

  • KGP Talkie
    January 17, 2021

    We missed you KGP Talkie.

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