• Corpus ID: 160018026

Analysis Using Natural Language Toolkit and VADER Sentiment

@inproceedings{ElbagirAnalysisUN,
  title={Analysis Using Natural Language Toolkit and VADER Sentiment},
  author={Shihab Elbagir and Jing Yang}
}
iaeng.org
52 Citations
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6 References

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This paper proposes a novel approach that goes deeper in the classification of texts collected from Twitter and classifies these texts into multiple sentiment classes, and proves to be very accurate in binary classification and ternary classification.

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

Interestingly, using the authors' parsimonious rule-based model to assess the sentiment of tweets, it is found that VADER outperforms individual human raters, and generalizes more favorably across contexts than any of their benchmarks.

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