Text-Classification

Natural Language Processing for Real-World Projects Real-world NLP starts with a practical goal. Rather than chasing the most powerful model, teams succeed by matching the solution to a real task, the data you have, and the tolerance for error. Start with a concrete problem, for example routing incoming emails by topic or classifying support tickets. Gather a small, representative sample and write clear labeling rules. A simple baseline, such as keyword rules or a basic text classifier, often shows you the right direction. ...

Sentiment Analysis: Understanding Opinions at Scale Sentiment analysis helps teams read opinions at scale by turning free text into simple signals. Companies use it to track how people feel about a product, a brand, or a campaign. The goal is to turn messy reviews and posts into reliable scores that guide decisions. To do this well, data quality matters. Start with data collection, then cleaning, annotation, and choosing an approach. You can use rule-based checks for clear phrases, or train models with labeled examples. In practice, many teams blend methods: a strong baseline model plus human review for edge cases. This mix keeps results practical and explainable. ...