VADER is a rule-based sentiment analysis model designed for social media text. It outperforms existing benchmarks and human raters in sentiment classification. The model uses a gold-standard lexicon of lexical features with sentiment intensity measures, combined with five general rules for sentiment intensity. VADER is validated using a combination of qualitative and quantitative methods, and it performs well in social media contexts, with a correlation coefficient of 0.881, comparable to human raters. It also achieves an F1 score of 0.96, surpassing human raters' F1 score of 0.84. VADER is simple, fast, and computationally efficient, making it suitable for real-time applications. It is also human-curated and validated, making it more interpretable and extendable. VADER is available for download and use. The model is compared to other sentiment analysis techniques, including machine learning approaches like Naive Bayes, Maximum Entropy, and Support Vector Machines. VADER's performance is evaluated across multiple domains, including social media text, movie reviews, product reviews, and opinion news articles. The results show that VADER performs as well as or better than other established sentiment analysis tools. VADER's simplicity and efficiency make it a valuable tool for sentiment analysis in various applications.VADER is a rule-based sentiment analysis model designed for social media text. It outperforms existing benchmarks and human raters in sentiment classification. The model uses a gold-standard lexicon of lexical features with sentiment intensity measures, combined with five general rules for sentiment intensity. VADER is validated using a combination of qualitative and quantitative methods, and it performs well in social media contexts, with a correlation coefficient of 0.881, comparable to human raters. It also achieves an F1 score of 0.96, surpassing human raters' F1 score of 0.84. VADER is simple, fast, and computationally efficient, making it suitable for real-time applications. It is also human-curated and validated, making it more interpretable and extendable. VADER is available for download and use. The model is compared to other sentiment analysis techniques, including machine learning approaches like Naive Bayes, Maximum Entropy, and Support Vector Machines. VADER's performance is evaluated across multiple domains, including social media text, movie reviews, product reviews, and opinion news articles. The results show that VADER performs as well as or better than other established sentiment analysis tools. VADER's simplicity and efficiency make it a valuable tool for sentiment analysis in various applications.