Sentiment Analysis

Sentiment analysis is an NLP technique that determines the emotional tone of texts. The system doesn't just detect whether a text is positive or negative - it can also identify specific emotions (joy, anger, disappointment) and their intensity. Modern systems also understand sarcasm, cultural nuances, and industry-specific expressions.

Sentiment analysis uses trained ML models that learned from large volumes of rated texts. The text to be analyzed is broken into tokens, and the model evaluates each word and its relationship to the context. Aspect-based sentiment analysis goes even deeper: it recognizes which specific aspects (service, price, quality) are rated positively or negatively - not just the overall impression.

Sentiment analysis transforms unstructured customer feedback into actionable data. Companies spot trends early, identify problem areas, and measure the impact of improvements. Especially valuable: automatic escalation on negative feedback - before an unhappy customer goes public.

A restaurant chain automatically analyzes all Google and Yelp reviews via sentiment analysis. The system detects that at the Hamburg location, ratings for 'wait time' have flipped from positive to negative over the past 4 weeks. The manager can respond specifically before the overall score drops.