Corpus ID: 12233345

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

  title={VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text},
  author={C. Hutto and E. Gilbert},
  • C. Hutto, E. Gilbert
  • Published in ICWSM 2014
  • Computer Science
  • The inherent nature of social media content poses serious challenges to practical applications of sentiment analysis. [...] Key Method Using a combination of qualitative and quantitative methods, we first construct and empirically validate a goldstandard list of lexical features (along with their associated sentiment intensity measures) which are specifically attuned to sentiment in microblog-like contexts.Expand Abstract
    1,405 Citations

    Figures, Tables, and Topics from this paper

    SenHint: A Joint Framework for Aspect-level Sentiment Analysis by Deep Neural Networks and Linguistic Hints
    • 4
    • PDF
    Sentiment Evaluation: User, Business Assessment and Hashtag Analysis
    • PDF
    Twitter as a Source for Time- and Domain-Dependent Sentiment Lexicons
    • 1
    Cross-Domain Sentiment Analysis on Social Media Interactions using Senti-Lexicon based Hybrid Features
    Text sentiment analysis using frequency-based vigorous features
    • 2


    Enhanced Sentiment Learning Using Twitter Hashtags and Smileys
    • 714
    • PDF
    A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs
    • 826
    • PDF
    A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts
    • 3,196
    • PDF
    Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
    • 4,102
    • PDF
    Opinion Mining and Sentiment Analysis
    • 4,916
    • PDF
    A holistic lexicon-based approach to opinion mining
    • 1,315
    • PDF
    Sentiment Analysis and Opinion Mining
    • B. Liu
    • Computer Science
    • Synthesis Lectures on Human Language Technologies
    • 2012
    • 1,610
    • PDF
    SenticNet: A Publicly Available Semantic Resource for Opinion Mining
    • 230
    • PDF