Text Classification with NLP
Classify text into categories using traditional ML and deep learning — sentiment analysis, spam detection, topic labeling, and intent classification. What is Text Classification? Text classification assigns predefined categories to text documents. Common applications: sentiment analysis (positive/negative/neutral), spam detection, topic labeling, intent classification for chatbots, and content moderation. It is one of the most widely deployed NLP tasks in production. Traditional Approaches Bag-of-Words (BoW) represents text as word frequency vectors. TF-IDF (Term Frequency-Inverse Document Frequency) downweights common words while highlighting discriminative terms. Train classifiers like Logistic Regression, Naive Bayes, or SVM on these features. These methods are fast, interpretable, and work well with limited data — still a strong baseline for many tasks. Deep Learning Approaches CNNs for text treat sentences as sequences of word embeddings — convolutions capture n-gram patterns. RNNs/LSTMs process text sequentially, capturing long-range dependencies. Pre-trained language models (BERT, RoBERTa, DistilBERT) fine-tune on classification tasks with minimal task-specific architecture. These models achieve state-of-the-art but require more compute. Best Practices Balance your dataset — use class weighting or oversampling for imbalanced classes. Clean text: remove HTML, normalize unicode, handle emojis. Use stratified k-fold cross-validation for reliable evaluation. Monitor for data drift — classification distributions can shift over time. Start with TF-IDF + linear model, only add deep learning if the baseline is insufficient for your accuracy requirements.