Document Open Access Logo

Predicting Performance Problems Through Emotional Analysis (Short Paper)

Authors Ricardo Martins , José João Almeida , Pedro Rangel Henriques , Paulo Novais



PDF
Thumbnail PDF

File

OASIcs.SLATE.2018.19.pdf
  • Filesize: 427 kB
  • 9 pages

Document Identifiers

Author Details

Ricardo Martins
  • Centro Algoritmi / Departamento de Informática, Universidade do Minho, Campus de Gualtar, Braga, Portugal
José João Almeida
  • Centro Algoritmi / Departamento de Informática, Universidade do Minho, Campus de Gualtar, Braga, Portugal
Pedro Rangel Henriques
  • Centro Algoritmi / Departamento de Informática, Universidade do Minho, Campus de Gualtar, Braga, Portugal
Paulo Novais
  • Centro Algoritmi / Departamento de Informática, Universidade do Minho, Campus de Gualtar, Braga, Portugal

Cite AsGet BibTex

Ricardo Martins, José João Almeida, Pedro Rangel Henriques, and Paulo Novais. Predicting Performance Problems Through Emotional Analysis (Short Paper). In 7th Symposium on Languages, Applications and Technologies (SLATE 2018). Open Access Series in Informatics (OASIcs), Volume 62, pp. 19:1-19:9, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/OASIcs.SLATE.2018.19

Abstract

In the cartoons, every time a character is nervous he/she begins to count to ten to keep calm. This is a technique, among hundreds, that helps to control the emotional state. However, what would be the impact if the emotions would not be controlled? Are the emotions important in terms of impairing the ability to perform tasks correctly? Using a case study of typing text, this paper is about a process to predict the number of writing errors from a person based on the emotional state and some characteristics of the writing process. Using preprocessing techniques, lexicon-based approaches and machine learning, we achieved a percentage of 80% of correct values, when considering the emotional profile on the writing style.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Natural language processing
Keywords
  • emotion analysis
  • machine learning
  • natural processing language

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Ritwik Banerjee, Song Feng, Jun Seok Kang, and Yejin Choi. Keystroke patterns as prosody in digital writings: A case study with deceptive reviews and essays. In Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1469-1473, 2014. Google Scholar
  2. Paul Ekman. An argument for basic emotions. Cognition &emotion, 6(3-4):169-200, 1992. Google Scholar
  3. Paul Ekman and Richard J. Davidson. The nature of emotion: Fundamental questions. Oxford University Press, 1994. Google Scholar
  4. Carroll E. Izard. Human emotions. Springer Science &Business Media, 2013. Google Scholar
  5. Matthew L. Jockers. Syuzhet: Extract Sentiment and Plot Arcs from Text, 2015. URL: https://github.com/mjockers/syuzhet.
  6. Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. The Stanford CoreNLP natural language processing toolkit. In 52nd Annual Meeting of the Association for Computational Linguistics, pages 55-60, 2014. Google Scholar
  7. Ricardo Martins, José João Dias de Almeida, Pedro Rangel Henriques, and Paulo Novais. Increasing authorship identification through emotional analysis. In Trends and Advances in Information Systems and Technologies, volume 745, pages 763-772. Springer International Publishing, 2018. URL: http://dx.doi.org/10.1007/978-3-319-77703-0_76.
  8. Robert R. McCrae and Oliver P. John. An introduction to the five-factor model and its applications. Journal of personality, 60(2):175-215, 1992. Google Scholar
  9. Scott Meier, Patricia R. McCarthy, and Ronald R. Schmeck. Validity of self-efficacy as a predictor of writing performance. Cognitive therapy and research, 8(2):107-120, 1984. Google Scholar
  10. Saif Mohammad and Peter D. Turney. Crowdsourcing a word-emotion association lexicon. Computational Intelligence, 29(3):436-465, 2013. URL: http://dx.doi.org/10.1111/j.1467-8640.2012.00460.x.
  11. Robert Plutchik. Emotion: A psychoevolutionary synthesis. Harpercollins College Division, 1980. Google Scholar
  12. Mike Thelwall, Kevan Buckley, Georgios Paltoglou, Di Cai, and Arvid Kappas. Sentiment strength detection in short informal text. Journal of the Association for Information Science and Technology, 61(12):2544-2558, 2010. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail