Text classification is a fundamental task in natural language processing (NLP) that involves categorizing text into predefined classes or labels. It’s widely used in applications like spam detection, sentiment analysis, and topic tagging.
📚 Applications of Text Classification
- Sentiment Analysis: Determining if a text is positive, negative, or neutral (e.g., movie reviews).
- Spam Filtering: Classifying emails or messages as spam or not spam.
- Topic Categorization: Grouping news articles into topics like sports, politics, or technology.
- Intent Recognition: Identifying user intent in chatbots or customer service systems.
🧠 Key Steps in Text Classification
- Data Collection: Gather labeled datasets (e.g., IMDB reviews for sentiment analysis).
- Preprocessing: Tokenization, stopword removal, and feature extraction (e.g., TF-IDF or word embeddings).
- Model Training: Using algorithms like Naive Bayes, SVM, or deep learning models (e.g., BERT).
- Evaluation: Metrics such as accuracy, precision, and recall to assess performance.
🖼️ Visualizing the Process
🧪 Example Models
- Traditional Models: Logistic Regression, Random Forest.
- Deep Learning Models: RNNs, Transformers (e.g., BERT).
- Pretrained Models: Use Hugging Face's transformer library for state-of-the-art results.
🌐 Expand Your Knowledge
- Explore NLP basics to understand foundational concepts.
- Learn about sentiment analysis for deeper insights.
Note: All images are illustrative and generated for educational purposes.