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VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text C.J. Hutto Eric Gilbert Georgia Institute of Technology, Atlanta, GA 30032 [email protected] [email protected] Abstract The inherent nature of social media content poses serious challenges to practical applications of sentiment analysis. We present VADER, a simple rule-based model for general sentiment analysis, and compare its effectiveness to eleven typical state-of-practice benchmarks including LIWC, ANEW, the General Inquirer, SentiWordNet, and machine learning oriented techniques relying on Naive Bayes, Max- imum Entropy, and Support Vector Machine (SVM) algo- rithms. Using a combination of qualitative and quantitative methods, we first construct and empirically validate a gold- standard list of lexical features (along with their associated sentiment intensity measures) which are specifically attuned to sentiment in microblog-like contexts. We then combine these lexical features with consideration for five general rules that embody grammatical and syntactical conventions for expressing and emphasizing sentiment intensity. Inter- estingly, using our parsimonious rule-based model to assess the sentiment of tweets, we find that VADER outperforms individual human raters ( F1 Classification Accuracy = 0.96 and 0.84, respectively), and generalizes more favorably across contexts than any of our benchmarks. 1. Introduction Sentiment analysis is useful to a wide range of problems that are of interest to human-computer interaction practi- tioners and researchers, as well as those from fields such as sociology, marketing and advertising, psychology, eco- nomics, and political science. The inherent nature of mi- croblog content - such as those observed on Twitter and Facebook - poses serious challenges to practical applica- tions of sentiment analysis. Some of these challenges stem from the sheer rate and volume of user generated social content, combined with the contextual sparseness resulting from shortness of the text and a tendency to use abbreviat- ed language conventions to express sentiments. A comprehensive, high quality lexicon is often essential for fast, accurate sentiment analysis on such large scales. An example of such a lexicon that has been widely used in the social media domain is the Linguistic Inquiry and Word Count (LIWC, pronounced “Luke”) (Pennebaker, Francis, & Booth, 2001; Pennebaker, Chung, Ireland, Gon- Copyright © 2014, Association for the Advancement of Artificial Intelli- gence ( ). All rights reserved. zales, & Booth, 2007). Sociologists, psychologists, lin- guists, and computer scientists find LIWC appealing be- cause it has been extensively validated. Also, its straight- forward dictionary and simple word lists are easily inspect- ed, understood, and extended if desired. Such attributes make LIWC an attractive option to researchers looking for a reliable lexicon to extract emotional or sentiment polarity from text. Despite their pervasive use for gaging sentiment in social media contexts, these lexicons are often used with little regard for their actual suitability to the domain.
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