Sentiment Analyzer Guide
A sentiment analyzer detects the emotional tone of text, classifying it as positive, negative, or neutral while also identifying specific emotions like joy, sadness, anger, fear, surprise, and disgust. Brands use sentiment analysis to monitor social media mentions, product reviews, and customer feedback at scale, turning unstructured text into actionable metrics. The tool runs entirely in your browser, so you can paste sensitive internal documents without sending them to a third-party API. Each analysis returns an overall sentiment score plus per-emotion percentages, giving you a nuanced picture rather than a single thumbs-up or thumbs-down. The analyzer tokenizes your text into sentences and words, then matches each token against curated lexicons that carry sentiment and emotion weights. Positive and negative keyword hits are tallied per sentence, and the aggregate score is normalized to a 0-100 scale. Emotion detection uses a multi-label approach where the same word can contribute to multiple emotions, so a phrase like "pleasantly surprised" registers both joy and surprise. Neutral sentences dilute the overall score, which is why a mixed review lands closer to the middle than a purely positive or negative one. The tool also computes a confidence ratio based on how many tokens in the text carry sentiment, so very short inputs produce lower-confidence results.