Overview
This lecture introduces text classification (auto-categorization) in natural language processing (NLP), discussing practical applications, data preparation, and building a basic machine learning text classifier using Python.
Introduction to Text Classification
- Text classification assigns text documents (from phrases to articles) into predefined, non-overlapping categories.
- Applications include chatbots, social media monitoring, and automatic email categorization (e.g., spam vs. non-spam).
Necessary Tools and Setup
- Python, PyCharm, NLTK (Natural Language Toolkit), and scikit-learn are required.
- The BBC news dataset is used, containing thousands of labeled text documents in five balanced categories: business, entertainment, politics, sports, tech.
Preparing the Dataset
- Text documents are organized in labeled subdirectories and then consolidated into a single dataset file.
- Each entry in the dataset is a tuple containing the label and the text content.
Text Preprocessing
- Punctuation is removed and text is converted to lowercase for normalization.
- Common, non-informative words (stop words) are filtered out using NLTK’s stopword list, with some custom additions.
Analyzing Word Frequency
- Frequency distributions of words are calculated to identify distinctive words for each category.
- Distinctive tokens in each category help in classification.
Feature Extraction and Vectorization
- Text documents are converted into numerical vectors (feature vectors), typically using word count (CountVectorizer).
- Each document is represented as a vector of word counts.
Building and Evaluating the Classifier
- Data is split into training (80%) and testing (20%) sets to evaluate classifier performance honestly.
- A Naive Bayes classifier is trained using labeled vectors.
- Classifier accuracy is assessed using precision, recall, and F1 score metrics, with high scores (≈97–99%) on this dataset.
Deployment and Prediction
- Once trained, the classifier and vectorizer are stored (pickled) for reuse without retraining.
- New, unseen texts can be classified with the trained model using a simple script.
Alternative Methods
- Other classifiers and vectorizers (like tf-idf) exist but may not outperform the basic count vectorizer for this task.
Key Terms & Definitions
- Text Classification — Assigning documents to predefined categories based on their content.
- Stop Words — Common words (e.g., "the", "and") that are filtered out as they carry little meaning for classification.
- Tokenization — The process of splitting text into individual words or ‘tokens.’
- Vectorizer — Converts text into a numerical vector format for machine learning.
- Naive Bayes Classifier — A probabilistic machine learning model based on Bayes' theorem, suitable for text classification.
- Precision, Recall, F1 Score — Evaluation metrics for classification performance.
Action Items / Next Steps
- Download the provided Python file from D2L and experiment with text classification.
- Prepare a labeled text dataset and try building your own classifier.
- Explore modifications to preprocessing steps or try different classifiers/vectorizers for further learning.