NLP_Text_Classification_With_ML

Natural Language Procession For Text Classification and Machine learning

Text Classification is one model of supervised machine learning task with a labelled dataset containing text documents and their labels is used for train a classifier.

Steps : -

1. Dataset Preparation:

Dataset Preparation step which includes the process of loading a dataset and performing basic pre-processing. The dataset is then splitted into train and validation sets.

2. Feature Engineering

In this step, raw text data will be transformed into feature vectors #and new features will be created using the existing dataset. We will implement the following different ideas in #order to obtain relevant features from our dataset.

2.1 Count Vectors as features

2.2 TF-IDF Vectors as features

-Word level

-N-Gram level

-Character level

2.3 Word Embeddings as features

2.4 Text / NLP based features

2.5 Topic Models as features

3. Model Building & Training:

Model Building step in which a machine learning model is trained on a labelled dataset.

-Naive Bayes Classifier

-Linear Classifier

-Support Vector Machine

-Bagging Models

-Boosting Models

-Shallow Neural Networks

-Deep Neural Networks

-Convolutional Neural Network (CNN)

-Long Short Term Modelr (LSTM)

-Gated Recurrent Unit (GRU)

-Bidirectional RNN

-Recurrent Convolutional Neural Network (RCNN)

-Other Variants of Deep Neural Networks

The diagnostic measures covered are:

1. accuracy: proportion of test results that are correct

2. sensitivity: proportion of true +ve identified

3. specificity: proportion of true -ve identified

4. positive likelihood: increased probability of true +ve if test +ve

5. negative likelihood: reduced probability of true +ve if test -ve

6. false positive rate: proportion of false +ves in true -ve patients

7. false negative rate: proportion of false -ves in true +ve patients

8. positive predictive value: chance of true +ve if test +ve

9. negative predictive value: chance of true -ve if test -ve

10. precision = positive predictive value

11. recall = sensitivity

12. f1 = (2 * precision * recall) / (precision + recall)

4. Improve Performance of Text Classifier:

we will use different ways to improve the performance of text classifiers.

5. Machine learning classifiers interpretability

Using ELI5