StackOverflow-Multilabel-Tag-Predictor

Overview

The SO Tag Predictor is a project designed to predict appropriate tags for Stack Overflow posts using advanced natural language processing (NLP) techniques and machine learning models. This tool is intended to assist developers and content moderators by automatically suggesting relevant tags for textual content.

Business Problem

Stack Overflow is a platform where users post questions on a variety of topics, and these posts are tagged for easier search and categorization. Manually tagging posts is time-consuming and prone to inconsistency. The SO multilabel Tag Predictor aims to automate this process, ensuring accurate and efficient tagging, thereby enhancing the user experience and improving content discoverability.

Features

• Text Preprocessing: Includes tokenization, stemming, and removal of stopwords.
• Feature Extraction: Uses methods such as Count Vectorization and TF-IDF Vectorization.
• Visualization: Implements data visualizations such as word clouds and other plots to explore the dataset.
• Machine Learning: Utilizes predictive models to classify and assign tags based on the post content.

Requirements

The following libraries and tools are required to run the project:

• Python (>=3.8)
• Libraries:
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • nltk
  • scikit-learn
  • wordcloud
  • sqlite3 or sqlalchemy for database interactions

Project Workflow

1. Data Collection and Loading:
   • Posts data is fetched from a database or a CSV file.
2. Data Cleaning:
   • Text is preprocessed by removing special characters, stopwords, and performing stemming.
3. Feature Extraction:
   • Text features are extracted using vectorization techniques.
4. Exploratory Data Analysis (EDA):
   • Visualizations such as word clouds and frequency distributions are created.
5. Model Building:
   • Machine learning algorithms are applied to train predictive models.
6. Evaluation:
   • Model performance is assessed using the following metrics:

Performance Metrics

1. Micro-Averaged F1-Score (Mean F1 Score):

   • The F1 score is the weighted average of precision and recall, where the F1 score reaches its best value at 1 and worst score at 0.
   • Formula:

     F1 = 2 * (precision * recall) / (precision + recall)
     

   • In the multi-class and multi-label case, this is the weighted average of the F1 score of each class.
   • Micro F1 Score: Calculates metrics globally by counting the total true positives, false negatives, and false positives. This is a better metric for class imbalance.
   • Macro F1 Score: Calculates metrics for each label and finds their unweighted mean. This does not consider label imbalance.

   References:

   • Mean F1 Score on Kaggle
   • Scikit-learn F1 Score

2. Hamming Loss:

   • The Hamming loss is the fraction of labels that are incorrectly predicted.
   • Reference: Hamming Loss on Kaggle

3. Prediction:

   • The trained model is used to predict tags for new input text.

Key Components

• Text Preprocessing:
  • Tokenization and stemming using nltk.
  • Stopword removal for cleaner input data.
• Feature Extraction:
  • Bag-of-Words (BoW) using CountVectorizer.
  • Term Frequency-Inverse Document Frequency (TF-IDF) using TfidfVectorizer.
• Visualization:
  • Word clouds to visualize frequent terms.
  • Statistical plots using matplotlib and seaborn.
• Machine Learning Models:
  • Model selection and hyperparameter tuning.

Results

• Demonstrates the ability to accurately predict tags for Stack Overflow posts.
• Provides insights into the most relevant features for tag prediction.

Source / Useful Links

• Data Source: Facebook Recruiting III - Keyword Extraction
• Research Papers:
  • Microsoft Research Paper
  • ACM Research Paper

Acknowledgements

This project leverages open-source libraries and publicly available datasets. Special thanks to Stack Overflow for its dataset and the developers of the Python libraries used in this project.