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Master the core techniques for assigning labels to text, from classical sentiment analysis and document classification to modern deep learning approaches including TextCNN and BERT fine-tuning. Learn multi-label strategies, evaluation metrics, and practical deployment considerations.
Text classification is one of the most widely deployed NLP tasks in industry. Every spam filter, content moderation system, and customer feedback pipeline relies on a classifier that maps raw text to one or more categorical labels. The task appears simple --- read some text, pick a label --- but building classifiers that generalize across domains, handle class imbalance, and scale to millions of documents requires deep understanding of both traditional and neural methods.
This chapter covers the full spectrum of text classification. We begin with sentiment analysis, the canonical classification task, then move to general document classification and multi-label settings where each document can belong to multiple categories simultaneously. On the modeling side, we explore CNNs for text (the TextCNN architecture that applies convolutional filters over word embeddings), fine-tuning pretrained transformers like BERT, and the evaluation metrics that matter for real-world deployment.
The progression mirrors how the field evolved: from hand-crafted features and linear classifiers, through task-specific neural architectures, to the current paradigm of fine-tuning large pretrained models. Understanding each stage helps you choose the right approach for your data size, latency requirements, and accuracy needs.
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Polarity classification, aspect-based sentiment, and fine-grained rating prediction.
Document-level and multi-label extensions of classification
Naive Bayes, TF-IDF features, hierarchical categories, and handling class imbalance.
Binary relevance, classifier chains, and threshold tuning for multi-label outputs.
1D convolution over embeddings, max-pooling, and the TextCNN architecture.
Pretrained model fine-tuning and evaluation methodology
Classification heads, learning rate strategies, and parameter-efficient fine-tuning.
Precision, recall, F1, macro/micro averaging, confusion matrices, and ROC-AUC.
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