Stack Overflow Question Topic Classification

This project aims to classify questions from Stack Overflow into predefined topics. Using data retrieved from the Stack Exchange API, we focus on building a model that can automatically label questions based on their content. This classification can be helpful for organizing questions, filtering topics, and improving content navigation on platforms with a high volume of user-generated content.

Project Overview

This project uses machine learning and natural language processing techniques to analyze and categorize Stack Overflow questions by topic. Each question in the dataset is tagged with one of a set of predefined topics, allowing the model to learn patterns associated with each category.

Key Steps in the Project:

1. Data Retrieval:

• Data is fetched using the Stack Exchange API, focusing on questions asked on Stack Overflow.
• Topics in this project include a subset of common areas, specifically:
  • Internet of Things
  • Software Development
  • Data Science and Artificial Intelligence Note: This project does not cover all possible topics available on Stack Overflow.

2. Data Preprocessing:

• Text Cleaning: Remove special characters, convert text to lowercase, and remove noise.
• Tokenization: Split questions into individual tokens (words) for better processing.
• Stemming/Lemmatization: Normalize words to their root forms.

3. Exploratory Data Analysis (EDA):

• Visualize the distribution of questions across the available topics to check for any class imbalance.
• Analyze common terms and question patterns to understand topic-specific language.

4. Feature Engineering:

• TF-IDF (Term Frequency-Inverse Document Frequency) is used to transform text data into numerical vectors, helping the model understand the importance of words in context. Model Training:

5. Train and test several classification algorithms for topic categorization, including:

• Naive Bayes: A probabilistic approach for text classification.
• Support Vector Machine (SVM): Known for effectiveness in high-dimensional spaces.
• Decision Trees / Random Forests: Useful for capturing non-linear relationships in the data.

6. Model Evaluation:

• Evaluate models with metrics like accuracy, precision, recall, and F1-score.
• Analyze results through confusion matrices and classification reports to see how well each model performs across different topics.

7. Conclusion and Future Work:

• Summarize model performance and identify areas for improvement, such as further parameter tuning or addressing class imbalance.

Project Limitations

This project is limited by focusing on only three topics (Internet of Things, Software Development, and Data Science and Artificial Intelligence), which represent only a subset of the vast array of topics on Stack Overflow. As such:

• Limited Coverage: The model does not generalize to all Stack Overflow topics, which restricts its application to only these selected areas.
• Potential for Class Imbalance: Certain topics may have significantly fewer questions, affecting model performance. Future expansions could include adding more topics, which would require additional data collection and re-evaluation of the model to handle a broader topic set.