VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text

VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text

2014 | C.J. Hutto Eric Gilbert
VADER (Valence Aware Dictionary for sEntiment Reasoning) is a rule-based model designed for sentiment analysis of social media text. The authors, C.J. Hutto and Eric Gilbert, present VADER as a simple and effective tool that outperforms several state-of-the-art benchmarks, including LIWC, ANEW, and machine learning techniques. They construct a gold-standard list of lexical features and their sentiment intensity measures, specifically tailored for microblog-like contexts. These features are combined with five general rules that embody grammatical and syntactical conventions for expressing and emphasizing sentiment intensity. VADER's performance is evaluated using qualitative and quantitative methods, and it is found to be highly accurate, outperforming individual human raters in sentiment classification tasks. The model is also computationally efficient and easily accessible, making it suitable for a wide range of applications. The paper discusses the development, validation, and evaluation of VADER, highlighting its advantages over existing sentiment analysis tools and its potential for practical use in various domains.VADER (Valence Aware Dictionary for sEntiment Reasoning) is a rule-based model designed for sentiment analysis of social media text. The authors, C.J. Hutto and Eric Gilbert, present VADER as a simple and effective tool that outperforms several state-of-the-art benchmarks, including LIWC, ANEW, and machine learning techniques. They construct a gold-standard list of lexical features and their sentiment intensity measures, specifically tailored for microblog-like contexts. These features are combined with five general rules that embody grammatical and syntactical conventions for expressing and emphasizing sentiment intensity. VADER's performance is evaluated using qualitative and quantitative methods, and it is found to be highly accurate, outperforming individual human raters in sentiment classification tasks. The model is also computationally efficient and easily accessible, making it suitable for a wide range of applications. The paper discusses the development, validation, and evaluation of VADER, highlighting its advantages over existing sentiment analysis tools and its potential for practical use in various domains.
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