Syllabus Map

• Study map: Syllabus Study Map

Overview

• Text classification assigns labels to documents or sentences.
• Typical tasks: sentiment analysis, topic labeling, spam/toxicity detection, intent classification.
• Labels can be single-label, multi-class, or multi-label.

Typical Pipeline

Representation

• Bag-of-words or TF-IDF.
• Embeddings or transformer encoders.

Classifier

• Logistic regression or linear SVM.
• Fine-tuned transformer.

Objective

• Single-label multi-class usually uses cross-entropy:

• Multi-label usually uses binary cross-entropy per class.

Step-by-Step Workflow

Step 1: Define label space

• Ensure class definitions are mutually exclusive (if single-label).
• Write annotation guidelines to reduce label noise.

Step 2: Split data correctly

• Use train/validation/test split before model tuning.
• Keep label distribution similar across splits.

Step 3: Build baseline

• Start with TF-IDF + linear classifier.
• This baseline is fast, interpretable, and often strong on small datasets.

Step 4: Fine-tune transformer

• Tokenize inputs with pretrained tokenizer.
• Add classification head on top of [CLS] (or pooled) embedding.
• Train end-to-end with cross-entropy.

Step 5: Evaluate and calibrate

• Evaluate on validation/test data using task-appropriate criteria.
• Tune decision thresholds for imbalanced or multi-label tasks.

Practical Notes

Handle class imbalance carefully

• Check class frequencies before training to identify majority/minority skew.
• Track per-class validation behavior, not only aggregate performance.
• Prioritize minority-class recall when those errors are costly.

Use proper train/validation splits

• Split data before tuning to avoid optimistic validation results.
• Preserve label distribution across splits when possible.
• Prevent near-duplicate texts from leaking across train and validation/test sets.

Use imbalance mitigation methods

• Apply class-weighted loss to penalize minority-class errors more.
• Consider focal loss when many easy examples dominate training.
• Use minority oversampling carefully to improve recall without overfitting.

Run targeted error analysis

• Inspect the confusion matrix to find systematic class confusions.
• Review false positives and false negatives by class to identify failure modes.
• Check tokenization and domain vocabulary coverage for missing signal.