The Impact of Lexicon Adaptation on the Emotion Mining From Software Engineering Artifacts - Publication - Bridge of Knowledge

Search

The Impact of Lexicon Adaptation on the Emotion Mining From Software Engineering Artifacts

Abstract

Sentiment analysis and emotion mining techniques are increasingly being used in the field of software engineering. However, the experiments conducted so far have not yielded high accuracy results. Researchers indicate a lack of adaptation of the methods of emotion mining to the specific context of the domain as the main cause of this situation. The article describes research aimed at examining whether the adaptation of the lexicon with emotional intensity of words in the context of software engineering improves the reliability of sentiment analysis. For this purpose, a new lexicon is developed in which words are evaluated as if they were used in the field of software engineering. A comparative experiment of emotion mining based on a generic and a software engineering specific lexicon does not reveal any significant differences in the results.

Citations

  • 8

    CrossRef

  • 0

    Web of Science

  • 9

    Scopus

Cite as

Full text

download paper
downloaded 41 times
Publication version
Accepted or Published Version
License
Creative Commons: CC-BY open in new tab

Keywords

Details

Category:
Articles
Type:
artykuły w czasopismach
Published in:
IEEE Access no. 8, pages 48742 - 48751,
ISSN: 2169-3536
Language:
English
Publication year:
2020
Bibliographic description:
Wróbel M.: The Impact of Lexicon Adaptation on the Emotion Mining From Software Engineering Artifacts// IEEE Access -Vol. 8, (2020), s.48742-48751
DOI:
Digital Object Identifier (open in new tab) 10.1109/access.2020.2979148
Bibliography: test
  1. C. D. Fisher and N. M. Ashkanasy, ''The emerging role of emotions in work life: An introduction,'' J. Org. Behav., vol. 21, no. 2, pp. 123-129, Mar. 2000. open in new tab
  2. R. A. Calvo and S. D'Mello, ''Affect detection: An interdisciplinary review of models, methods, and their applications,'' IEEE Trans. Affect. Comput., vol. 1, no. 1, pp. 18-37, Jan. 2010. open in new tab
  3. M. R. Wrobel, ''Emotions in the software development process,'' in Proc. 6th Int. Conf. Hum. Syst. Interact. (HSI), Jun. 2013, pp. 518-523. open in new tab
  4. D. Graziotin, X. Wang, and P. Abrahamsson, ''Are happy developers more productive?'' in Proc. 14th Int. Conf. PROFES, 2013, pp. 50-64. open in new tab
  5. I. A. Khan, R. M. Hierons, and W. P. Brinkman, ''Mood independent programming,'' in Proc. 14th Eur. Conf. Cognit. Ergonom. Invent! Explore! (ECCE), 2007, pp. 269-272. open in new tab
  6. M. R. Wrobel, ''Towards the participant observation of emotions in soft- ware development teams,'' in Proc. Federated Conf. Comput. Sci. Inf. Syst. (FedCSIS), Sep. 2016, pp. 1545-1548. open in new tab
  7. M. R. Wrobel, ''Applicability of emotion recognition and induction meth- ods to study the behavior of programmers,'' Appl. Sci., vol. 8, no. 3, p. 323, 2018. open in new tab
  8. E. Cambria, ''Affective computing and sentiment analysis,'' IEEE Intell. Syst., vol. 31, no. 2, pp. 102-107, Mar./Apr. 2016. open in new tab
  9. L. Yue, W. Chen, X. Li, W. Zuo, and M. Yin, ''A survey of sentiment analysis in social media,'' Knowl. Inf. Syst., 2018, pp. 1-47. open in new tab
  10. R. Feldman, ''Techniques and applications for sentiment analysis,'' Com- mun. ACM, vol. 56, no. 4, p. 82, Apr. 2013. open in new tab
  11. B. Liu, ''Sentiment analysis and subjectivity,'' in Handbook of Natural Language Processing, 2nd Ed. London, U.K.: Chapman & Hall, 2010, pp. 627-666.
  12. U. Krcadinac, P. Pasquier, J. Jovanovic, and V. Devedzic, ''Synesketch: An open source library for sentence-based emotion recognition,'' IEEE Trans. Affect. Comput., vol. 4, no. 3, pp. 312-325, Jul. 2013. open in new tab
  13. F. Calefato, F. Lanubile, F. Maiorano, and N. Novielli, ''Sentiment polarity detection for software development,'' Empirical Softw. Eng., vol. 23, no. 3, pp. 1352-1382, Jun. 2018. open in new tab
  14. M. R. Islam and M. F. Zibran, ''Leveraging automated sentiment analysis in software engineering,'' in Proc. IEEE/ACM 14th Int. Conf. Mining Softw. Repositories (MSR), May 2017, pp. 203-214. open in new tab
  15. K. Sailunaz, M. Dhaliwal, J. Rokne, and R. Alhajj, ''Emotion detection from text and speech: A survey,'' Social Netw. Anal. Mining, vol. 8, no. 1, p. 28, Dec. 2018. open in new tab
  16. A. Murgia, P. Tourani, B. Adams, and M. Ortu, ''Do developers feel emotions? An exploratory analysis of emotions in software artifacts,'' in Proc. 11th Work. Conf. Mining Softw. Repositories (MSR), 2014, pp. 262-271. open in new tab
  17. A. Yadollahi, A. G. Shahraki, and O. R. Zaiane, ''Current state of text sentiment analysis from opinion to emotion mining,'' ACM Comput. Surv., vol. 50, no. 2, pp. 1-33, May 2017. open in new tab
  18. R. Jongeling, S. Datta, and A. Serebrenik, ''Choosing your weapons: On sentiment analysis tools for software engineering research,'' in Proc. IEEE Int. Conf. Softw. Maintenance Evol. (ICSME), Sep. 2015, pp. 531-535. open in new tab
  19. P. Tourani, Y. Jiang, and B. Adams, ''Monitoring sentiment in open source mailing lists: Exploratory study on the apache ecosystem,'' in Proc. 24th open in new tab
  20. Annu. Int. Conf. Comput. Sci. Softw. Eng., 2014, pp. 34-44. open in new tab
  21. N. Novielli, F. Calefato, and F. Lanubile, ''The challenges of sentiment detection in the social programmer ecosystem,'' in Proc. 7th Int. Workshop Social Softw. Eng. (SSE), 2015, pp. 33-40. open in new tab
  22. S. Owsley, S. Sood, and K. J. Hammond, ''Domain specific affective classi- fication of documents,'' in Proc. AAAI Spring Symp., Comput. Approaches Analyzing Weblogs, 2006, pp. 181-183. open in new tab
  23. M. V. Mäntylä, N. Novielli, F. Lanubile, M. Claes, and M. Kuutila, ''Boot- strapping a lexicon for emotional arousal in software engineering,'' in Proc.
  24. IEEE/ACM 14th Int. Conf. Mining Softw. Repositories. Piscataway, NJ, USA: IEEE Press, May 2017, pp. 198-202. open in new tab
  25. M. R. Islam and M. F. Zibran, ''SentiStrength-SE: Exploiting domain specificity for improved sentiment analysis in software engineering text,'' J. Syst. Softw., vol. 145, pp. 125-146, Nov. 2018. open in new tab
  26. J. Ding, H. Sun, X. Wang, and X. Liu, ''Entity-level sentiment analysis of issue comments,'' in Proc. 3rd Int. Workshop Emotion Awareness Softw. Eng. (SEmotion), 2018, pp. 7-13. open in new tab
  27. B. Lin, F. Zampetti, G. Bavota, M. Di Penta, M. Lanza, and R. Oliveto, ''Sentiment analysis for software engineering: How far can we go?'' in Proc. IEEE/ACM 40th Int. Conf. Softw. Eng., May/Jun. 2018, pp. 94-104. open in new tab
  28. N. Imtiaz, J. Middleton, P. Girouard, and E. Murphy-Hill, ''Sentiment and politeness analysis tools on developer discussions are unreliable, but so are people,'' in Proc. 3rd Int. Workshop Emotion Awareness Softw. Eng. (SEmotion), 2018, pp. 55-61. open in new tab
  29. E. Cambria, B. Schuller, Y. Xia, and C. Havasi, ''New avenues in opin- ion mining and sentiment analysis,'' IEEE Intell. Syst., vol. 28, no. 2, pp. 15-21, Mar. 2013. open in new tab
  30. L. Gatti, M. Guerini, and M. Turchi, ''SentiWords: Deriving a high preci- sion and high coverage lexicon for sentiment analysis,'' IEEE Trans. Affect. Comput., vol. 7, no. 4, pp. 409-421, Oct. 2016. open in new tab
  31. M. M. Bradley and P. J. Lang, ''Affective norms for english words (anew): Instruction manual and affective ratings,'' Center Research Psychophysi- ology, Univ. Florida, Gainesville, Fl, USA, Tech. Rep. C-1, 1999.
  32. A. Kołakowska, A. Landowska, M. Szwoch, W. Szwoch, and M. R. Wrobel, ''Modeling emotions for affect-aware applications,'' Inf. Syst. Develop. Appl., pp. 55-67, Jan 2015. [Online]. Available: http://wzr.ug.edu.pl/nauka/upload/files/Information%20systems%20 development%20and%20applications.pdf#page=55 open in new tab
  33. A. Landowska, ''Towards new mappings between emotion representation models,'' Appl. Sci., vol. 8, no. 2, p. 274, 2018. open in new tab
  34. I. Salman, A. T. Misirli, and N. Juristo, ''Are students representatives of professionals in software engineering experiments?'' in Proc. 37th Int. Conf. Softw. Eng., vol. 1. Piscataway, NJ, USA: IEEE Press, May 2015, pp. 666-676. open in new tab
  35. F. Jurado and P. Rodriguez, ''Sentiment analysis in monitoring software development processes: An exploratory case study on Github's project issues,'' J. Syst. Softw., vol. 104, pp. 82-89, Jun. 2015. open in new tab
  36. M. M. Bradley and P. J. Lang, ''Measuring emotion: The self-assessment manikin and the semantic differential,'' J. Behav. Therapy Experim. Psy- chiatry, vol. 25, no. 1, pp. 49-59, Mar. 1994. open in new tab
  37. D. Graziotin, X. Wang, and P. Abrahamsson, ''Understanding the affect of developers: Theoretical background and guidelines for psychoempirical software engineering,'' in Proc. 7th Int. Workshop Social Softw. Eng. (SSE), 2015, pp. 25-32. open in new tab
  38. P. S. Dodds and C. M. Danforth, ''Measuring the happiness of large- scale written expression: Songs, blogs, and presidents,'' J. Happiness Stud., vol. 11, no. 4, pp. 441-456, Aug. 2010. open in new tab
  39. A. Neviarouskaya and M. Aono, ''Sentiment word relations with affect, judgment, and appreciation,'' IEEE Trans. Affect. Comput., vol. 4, no. 4, pp. 425-438, Oct./Dec. 2013. open in new tab
  40. MICHAL R. WROBEL was born in Gdynia, Poland, in 1978. He received the M.S. and engineering degrees in computer science from the Gdańsk University of Technology, Poland, in 2002, and the Ph.D. degree in computer sci- ence from the Gdańsk University of Technology, in 2011. Since 2006, he has been with the Faculty of Electronics, Telecommunications and Informat- ics, Department of Software Engineering, Gdańsk University of Technology. He is currently a member of the Emotions in HCI Research Group, where he conducts research on the software usability, affec- tive computing, and software management methods. His research interest includes a modern approach to software development management, with a particular focus on the role of the human factors in software engineering.
Verified by:
Gdańsk University of Technology

seen 101 times

Recommended for you

Meta Tags