Text Classification is an NLP (Natural Language Processing) task where the goal is to assign predefined categories or labels to a given piece of text.
What does it mean as a problem?
- Input: Raw text (sentence, paragraph, document, tweet, review, etc.)
- Output: One or more class labels (e.g., “spam” vs. “not spam”, or “positive” vs. “negative” sentiment).
- Goal: Learn a mapping from text to classes using a model trained on labeled data.
Formally, given:
Find a function: .
Examples of Text Classification Tasks
- Spam Detection: Classify email as spam or ham.
- Sentiment Analysis: Positive / Negative / Neutral review classification.
- Topic Classification: News articles → Sports, Politics, Tech.
- Intent Detection: In chatbots, classify user query as booking, cancellation, etc.
- Toxic Comment Detection: Safe vs. abusive language.
Why is it challenging?
- Text is unstructured: needs preprocessing (tokenization, encoding). See Preprocessing Text Classification
- Vocabulary is large and sparse.
- Ambiguity and context sensitivity in language.
- Requires feature extraction (e.g., Bag of Words, TF-IDF, word embeddings).
Typical Approaches
- Classical ML: Naive Bayes, Logistic Regression, SVM with Bag-of-Words or TF-IDF features.