Multiclass Multilabel Prediction For StackOverflow Questions

Data set : https://www.kaggle.com/saikrishnapulipati/multilable-multiclass-prediction-for-stackoverflow/data

Objective : predict tags associated with them.

This is a scaled down version of predecting only top 10 most occurring tags

Programming Language : Python using nltk & Keras

Model Architecture : Deep Learning using Recurrent Neural Network (RNN)

About Data Set

Dataset has text of questionsand thier corresponding tags from the Stack Overflow programming Q&A website.

This is organized as three files:

1. Questions contains the title, body, creation date, closed date (if applicable), score, and owner ID for all non-deleted Stack Overflow questions.

2. Tags contains the tags on each of these questions.

3. Answers contains the body, creation date, score, and owner ID for each of the answers to these questions. The ParentId column links back to the Questions table. We don't use this file as we want to predict Tags given a question Data Pre-Processing

Tags File Code : Stackoverflow Tags Map & Model.ipynb

1. Read Tags File
2. Identify top 10 Tags by count
3. Manipulate the tags dataframe so that all the Tags for an ID are as a list in a row (grouped by Question ID). Our Dataset would now have only Id, Title, Body & Tags

Text Preprocessing Code : Stackoverflow Tags Map & Model.ipynb

We will use nltk, preprocessing from Keras and sklearn to process the text data

Tags preprocesing Use MultiLabelBinarizer from sklearn on the Class labels(Tags)

from sklearn.preprocessing import MultiLabelBinarizer
multilabel_binarizer = MultiLabelBinarizer()
multilabel_binarizer.fit(total.Tags)
print(multilabel_binarizer.classes_)

array(['android', 'c#', 'c++', 'html', 'ios', 'java', 'javascript','jquery', 'php', 'python'], dtype=object)

Title & Body Preprocessing

1. Tokenize the words
2. Convert the tokenized words to sequences

Model Building

Implemented a Hybrid model in TensorFlow using Keras as high level api. Architecture used is RNN. In this model first we train a model using the Title data, then train a model using the Body data. Outputs of both are concatenated and passed thorugh the dense layers before connecting to the output layer

RNN Model : The model first uses GRU for the sequence data training with 2 GRU layers one for Title and other for Body.

RNN for Title has

• 1 Embedding Layer has input of Title vocabulary length(68969) + 1(for 0 padding) and out put of 2000 embeddings (for better results use full vocabulary length+1)
• 1 Gated recurrent unit (GRU) layer
• 1 dense output layer of shape 10(No of classes(tags) we are trying to predict) RNN for Body has
• 1 Embedding Layer has input of Title vocabulary length(1292018) + 1(for 0 padding) and out put of 170 embeddings (for better results use full vocabulary length+1)
• 1 Gated recurrent unit (GRU) layer Combine the 2 GRU outputs

The fully connected network has

• 2 Dense Layers
• 1 Dropout layer
• 1 BatchNormalization layer
• 1 Dense Output layer Model Compilattion with optimizer='adam', loss='categorical_crossentropy', metrics='accuracy')

Model Performance Review

Classification Report to check Precision, Recall and F1 Score

The Model seem to performing good enough with score of 84%. Increase in the Embedding, GRU and dense layers would help in getting better results Save the Model & Weights

Saving the model for transfer learning or model execution later

model.save('./stackoverflow_tags.h5')