In this post, I will attempt to provide a method of comparison between several sentiment analysis approaches.



If I bring you home to mama
I guess I'd better warn ya
She falls in love a little faster than I do
And my dad will check your tires
Pour you whiskey over ice and
Take you fishing but pretend that he don't like you
Oh, if we break up, I'll be fine
But you'll be breaking more hearts than mine

The above is a particularly, emotional, at least I think so, line in the song “Breaking More Hearts than Mine” by (More Hearts Than Mine by Ingrid Andress | Billboard The Hot 100 Chart, 2019). We will run some analysis on it, comparing several approaches.

if (!require("pacman")) install.packages("pacman")
pacman::p_load_current_gh("trinker/lexicon", "trinker/sentimentr")

References

  1. More Hearts Than Mine by Ingrid Andress | Billboard The Hot 100 Chart. (2019). /charts/hot-100/2020-03-14
  2. Sliwa, P. (2012). In Defense of Moral Testimony. Philosophical Studies, 158(2), 175–195. https://doi.org/10.1007/s11098-012-9887-6
  3. Kiritchenko, S., & Mohammad, S. M. Happy Accident: A Sentiment Composition Lexicon for Opposing Polarity Phrases. 8.
  4. Kiritchenko, S., Zhu, X., & Mohammad, S. M. (2014). Sentiment Analysis of Short Informal Texts. Journal of Artificial Intelligence Research (JAIR), 50, 723–762.
  5. Kiritchenko, S., Zhu, X., & Mohammad, S. M. (2014). Sentiment Analysis of Short Informal Texts. Journal of Artificial Intelligence Research, 50, 723–762. https://doi.org/10.1613/jair.4272
  6. Kiritchenko, S., & Mohammad, S. (2016). The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition. Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 43–52. https://doi.org/10.18653/v1/W16-0410
  7. Kiritchenko, S., & Mohammad, S. M. (2016). Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best–Worst Scaling. Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL).
  8. Kiritchenko, S., & Mohammad, S. M. (2016). The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition. Proceedings of the Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA).
  9. Kiritchenko, S., & Mohammad, S. M. (2016). Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best–Worst Scaling. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 811–817. https://doi.org/10.18653/v1/N16-1095
  10. Kiritchenko, S., Mohammad, S. M., & Salameh, M. (2016, June). Semeval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases. Proceedings of the International Workshop on Semantic Evaluation.
  11. Kiritchenko, S., Mohammad, S., & Salameh, M. (2016). SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 42–51. https://doi.org/10.18653/v1/S16-1004
  12. Kiritchenko, S., & Mohammad, S. M. (2016). Sentiment Composition of Words with Opposing Polarities. Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), San Diego, California.
  13. Kiritchenko, S., & Mohammad, S. M. (2016). Sentiment Composition of Words with Opposing Polarities. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1102–1108. https://doi.org/10.18653/v1/N16-1128
  14. Kiritchenko, S., & Mohammad, S. M. (2016). Happy Accident: A Sentiment Composition Lexicon for Opposing Polarity Phrases. Proceedings of 10th Edition of the the Language Resources and Evaluation Conference (LREC).
  15. Kiritchenko, S., Mohammad, S. M., & Salameh, M. (2016, June). Semeval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases. Proceedings of the International Workshop on Semantic Evaluation.
  16. Kiritchenko, S., Mohammad, S., & Salameh, M. (2016). SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 42–51. https://doi.org/10.18653/v1/S16-1004
  17. Mohammad, S. M., & Turney, P. D. Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon. 9.
  18. Mohammad, S. M., & Turney, P. D. (2013). Crowdsourcing a Word-Emotion Association Lexicon. Computational Intelligence, 29(3), 436–465.
  19. Mohammad, S. M. (2018). Word Affect Intensities. Proceedings of the 11th Edition of the Language Resources and Evaluation Conference (LREC-2018).
  20. Mohammad, S. (2018). Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 174–184. https://doi.org/10.18653/v1/P18-1017
  21. Mohammad, S. M. (2018). Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words. Proceedings of the Annual Conference of the Association for Computational Linguistics (ACL).
  22. Mohammad, S. M. (2021). Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Text. http://arxiv.org/abs/2005.11882
  23. Rinker, T., Dame, U. of N., Technologies, D. of K., Unicode, Inc, Higgins, J., Ward, G., Possel, H., Mechura, M. B., Liu, B., Hu, M., Mohammad, S. M., Turney, P., Cambria, E., Poria, S., Bajpai, R., Schuller, B., SentiWordNet, Wu, L., … Malaescu, I. (2019). Lexicon: Lexicons for Text Analysis (Version 1.2.1). https://CRAN.R-project.org/package=lexicon
  24. Rosenthal, S., Nakov, P., Kiritchenko, S., Mohammad, S. M., Ritter, A., & Stoyanov, V. SemEval-2015 Task 10: Sentiment Analysis in Twitter. 13.
  25. Rosenthal, S., Nakov, P., Kiritchenko, S., Mohammad, S., Ritter, A., & Stoyanov, V. (2015). SemEval-2015 Task 10: Sentiment Analysis in Twitter. Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 451–463. http://www.aclweb.org/anthology/S15-2078
  26. Hopp, F. R., Fisher, J. T., Cornell, D., Huskey, R., & Weber, R. (2021). The Extended Moral Foundations Dictionary (eMFD): Development and Applications of a Crowd-Sourced Approach to Extracting Moral Intuitions from Text. Behavior Research Methods, 53(1), 232–246. https://doi.org/10.3758/s13428-020-01433-0
  27. Graham, J., Nosek, B. A., Haidt, J., Iyer, R., Koleva, S., & Ditto, P. H. (2011). Mapping the Moral Domain. Journal of Personality and Social Psychology, 101(2), 366–385. https://doi.org/10.1037/a0021847