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Automatic Myanmar News Classification System using Multinomial Naive Bayes

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Automatic Myanmar News Classification

Project Overview

Automatic Myanmar News Classification System using Linear SVM. I have examined also with other machine learning algorithms - Logistic Regression, Multinomial Naive Bayes, Random Forest and Decision Tree. The weighted f-score is highest when using Linear SVM.

  • A.H.Khine, K.T.Nwet, K.M.Soe, Automatic Myanmar News Classification proposed a system which is based on Naive Bayes. I used their dataset for training the model.[1]
  • Tokenzation is done by using pyidaungsu library which is based on fasttext.
  • The vecotorizer I used is tf-idf.
  • N-gram for TF-IDF is Unigram + Bigram

System Design

  • I use the system design proposed in Nwet, Khin & Darren, Seth, Machine Learning Algorithms for Myanmar News Classification [2]

System

Dataset

Dataset is taken from Aye Hnin Khine's repository Dataset

Experiments

  • For feature extraction, vectorize text data using TF-IDF vectorizer available in scikit-learn
  • Then train on different machine learning models for classification
Model F1-score
Decision Tree 67%
Random Forest 82%
Multinomial Naive Bayes 84%
Logistic Regression 86%
Linear SVM 88%

Demonstration

Demonstration available HERE Demo

How to run demo

  • Download this repository
  • Install requirements
tra@thura-pc:~$ pip install -r requirements.txt
  • Run the main notebook - News_Classificaiton.ipynb for training
  • For Demo
tra@thura-pc:~$ streamlit run app.py

Future Works

  • More Data is needed to
  • Test with Hybrid methods and Deep Learning Approaches

References

[1] A.H.Khine, K.T.Nwet, K.M.Soe, Automatic Myanmar News Classification, 15th Proceedings of International Conference on Computer Applications, February 2017, pp. 401-408
[2] Nwet, Khin & Darren, Seth. (2019). MACHINE LEARNING ALGORITHMS FOR MYANMAR NEWS CLASSIFICATION. Journal of Natural Language Processing. 8. 17-24.

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