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Sentiment Analysis in Javascript using the various lexicons including AFINN-165, VADER, NRC Word-Emotion Association, Bing and Loughran-McDonald

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Sentiment Analysis

GitHub code size in bytes contributions welcome Maintenance GitHub issues

Sentiment Analysis in Javascript using the various lexicons including AFINN-165, VADER, NRC Word-Emotion Association, Bing and Loughran-McDonald.

Objective

Providing a simple web interface to perform simple sentiment analysis.

Demo

Please visit here

Lexicons

  • AFINN-165
  • VADER
  • NRC Word-Emotion Association
  • Bing
  • Loughran-McDonald

Libraries

  • Bootstrap5 for Front End UI
  • No node modules.

Contributions

  • Open to contributions of new lexicons and improvements to the code base.

References

  • Finn Årup Nielsen, "A new ANEW: evaluation of a word list for sentiment analysis in microblogs", Proceedings of the ESWC2011 Workshop on 'Making Sense of Microposts': Big things come in small packages. Volume 718 in CEUR Workshop Proceedings: 93-98. 2011 May. Matthew Rowe, Milan Stankovic, Aba-Sah Dadzie, Mariann Hardey (editors)

  • Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.

  • Lexicon source is (C) 2016 National Research Council Canada (NRC) and this package is for research purposes only. Source: http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm per the terms of use of the NRC Emotion Lexicon, if you use the lexicon or any derivative from it, cithis paper: Crowdsourcing a Word-Emotion Association Lexicon, Saif Mohammad and Peter Turney, ComputationIntelligence, 29 (3), 436-465, 2013.

  • Minqing Hu and Bing Liu, ``Mining and summarizing customer reviews.'', Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD-2004), SeattlWashington, USA, Aug 22-25, 2004.

  • Word Affect Intensities. Saif M. Mohammad. In Proceedings of the 11th Edition of the Language Resources and Evaluation Conference (LREC-2018), May 2018, Miyazaki, Japan.

  • Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words. Saif M. Mohammad. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, July 2018.