skip to main content
10.1145/2901739.2901781acmconferencesArticle/Chapter ViewAbstractPublication PagesicseConference Proceedingsconference-collections
research-article

Sentiment analysis in tickets for IT support

Published:14 May 2016Publication History

ABSTRACT

Sentiment analysis has been adopted in software engineering for problems such as software usability and sentiment of developers in open-source projects. This paper proposes a method to evaluate the sentiment contained in tickets for IT (Information Technology) support.IT tickets are broad in coverage (e.g. infrastructure, software), and involve errors, incidents, requests, etc. The main challenge is to automatically distinguish between factual information, which is intrinsically negative (e.g. error description), from the sentiment embedded in the description. Our approach is to automatically create a Domain Dictionary that contains terms with sentiment in the IT context, used to filter terms in ticket for sentiment analysis. We experiment and evaluate three approaches for calculating the polarity of terms in tickets. Our study was developed using 34,895 tickets from five organizations, from which we randomly selected 2,333 tickets to compose a Gold Standard. Our best results display an average precision and recall of 82.83% and 88.42%, which outperforms the compared sentiment analysis solutions.

References

  1. S. Baccianella, A. Esuli, and F. Sebastiani. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proc. of the International Conference on Language Resources and Evaluation (LREC), Valletta, Malta, 2010.Google ScholarGoogle Scholar
  2. A. Balahur, R. Steinberger, M. Kabadjov, V. Zavarella, E. Van Der Goot, M. Halkia, B. Pouliquen, and J. Belyaeva. Sentiment analysis in the news. In Proc. of the International Conference on Language Resources and Evaluation (LREC), 2010, Valletta, Malta, volume 10, page 2216, 2010.Google ScholarGoogle Scholar
  3. A. Balahur and M. Turchi. Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis. Computer Speech & Language, 28(1):56--75, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. D. Bollegala, D. Weir, and J. Carroll. Cross-domain sentiment classification using a sentiment sensitive thesaurus. IEEE Transactions on Knowledge and Data Engineering, 25(8): 1719--1731, Aug 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. Bollen, H. Mao, and X. Zeng. Twitter mood predicts the stock market. Journal of Computational Science, 2(1):1--8, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  6. P. Ekman. An argument for basic emotions. Cognition & emotion, 6(3-4):169--200, 1992.Google ScholarGoogle ScholarCross RefCross Ref
  7. A. M. El-Halees. Software Usability Evaluation Using Opinion Mining. Journal of Software, 9(2):343--349, feb 2014.Google ScholarGoogle ScholarCross RefCross Ref
  8. C. Fellbaum. WordNet: An Electronic Lexical Database. Bradford Books, 1998.Google ScholarGoogle ScholarCross RefCross Ref
  9. N. Godbole, M. Srinivasaiah, and S. Skiena. Large-scale sentiment analysis for news and blogs. In Proc. of the First International AAAI Conference on Weblogs and Social Media (ICWSM), volume 2, 2007.Google ScholarGoogle Scholar
  10. E. Guzman, D. Azócar, and Y. Li. Sentiment analysis of commit comments in github: an empirical study. In Proc. of the 11th Working Conference on Mining Software Repositories, MSR 2014, Hyderabad, India, pages 352--355, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. E. Guzman and B. Bruegge. Towards Emotional Awareness in Software Development Teams. In Proc. of the 9th Joint Meeting on Foundations of Software Engineering, pages 671--674, New York, New York, USA, aug 2013. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Hu and B. Liu. Mining and summarizing customer reviews. In Proc. of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pages 168--177. ACM, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. R. Jongeling and A. Serebrenik. Choosing Your Weapons: On Sentiment Analysis Tools for Software Engineering Research. In Proc. of the IEEE International Conference on Software Maintenance and Evolution, pages 531--535, Bremen, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. F. Jurado and P. Rodriguez. Sentiment Analysis in monitoring software development processes: An exploratory case study on GitHub's project issues. Journal of Systems and Software, 104:82--89, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. N. Kobayashi, K. Inui, and Y. Matsumoto. Extracting aspect-evaluation and aspect-of relations in opinion mining. In EMNLP-CoNLL 2007, Proc. of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, June 28-30, 2007, Prague, Czech Republic, pages 1065--1074, 2007.Google ScholarGoogle Scholar
  16. B. Liu. Sentiment analysis and opinion mining. Morgan & Claypool Publishers, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. S. M. Mohammad. Sentiment analysis: Detecting valence, emotions, and other affectual states from text. In H. Meiselman, editor, Emotion Measurement. Elsevier, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  18. M. D. Molina-Gonzalez, E. Martinez-Camara, M.-T. Martin-Valdivia, and J. M. Perea-Ortega. Semantic orientation for polarity classification in spanish reviews. Expert Systems with Applications, 40(18): 7250--7257, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  19. A. Murgia, P. Tourani, B. Adams, and M. Ortu. Do developers feel emotions? an exploratory analysis of emotions in software artifacts. In Proc. of the 11th Working Conference on Mining Software Repositories, MSR 2014, Hyderabad, India, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. M. Ortu, B. Adams, G. Destefanis, P. Tourani, M. Marchesi, and R. Tonelli. Are Bullies more Productive? Empirical Study of Affectiveness vs. Issue Fixing Time. In Proc. of the IEEE/ACM Working Conference on Mining Software Repositories, Florence, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. D. Pletea, B. Vasilescu, and A. Serebrenik. Security and Emotion: Sentiment Analysis of Security Discussions on GitHub. In Proc. of the IEEE/ACM Working Conference on Mining Software Repositories, pages 348--351, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. G. Qiu, B. Liu, J. Bu, and C. Chen. Expanding domain sentiment lexicon through double propagation. In Proceedings of the 21st International Jont Conference on Artifical Intelligence, IJCAI'09, pages 1199--1204, San Francisco, CA, USA, 2009. Morgan Kaufmann Publishers Inc. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. S. Sahibudin, M. Sharifi, and M. Ayat. Combining itil, cobit and iso/iec 27002 in order to design a comprehensive it framework in organizations. In Proc. of the Second Asia International Conference on Modeling Simulation, 2008, pages 749--753, May 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. M. Silva, P. Carvalho, and L. Sarmento. Building a sentiment lexicon for social judgement mining. Computational Processing of the Portuguese Language, pages 218--228, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. J. Steinberger, M. Ebrahim, M. Ehrmann, A. Hurriyetoglu, M. Kabadjov, P. Lenkova, R. Steinberger, H. Tanev, S. VÃązquez, and V. Zavarella. Creating sentiment dictionaries via triangulation. Decision Support Systems, 53(4):689--694, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, and A. Kappas. Sentiment in short strength detection informal text. J. Am. Soc. Inf. Sci. Technol., 61(12): 2544--2558, Dec. 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. M. Tsytsarau and T. Palpanas. Survey on mining subjective data on the web. Data Mining and Knowledge Discovery, 24(3):478--514, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. D. Tumitan and K. Becker. Sentiment-based features for predicting election polls: A case study on the brazilian scenario. In Proc. of the IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014, volume 2, pages 126--133, Aug 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. A. B. Warriner, V. Kuperman, and M. Brysbaert. Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior research methods, 45(4): 1191--207, Dec. 2013.Google ScholarGoogle Scholar
  30. J. Wiebe, T. Wilson, and C. Cardie. Annotating expressions of opinions and emotions in language. Language resources and evaluation, 39(2-3):165--210, 2005.Google ScholarGoogle Scholar

Index Terms

  1. Sentiment analysis in tickets for IT support

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in
            • Published in

              cover image ACM Conferences
              MSR '16: Proceedings of the 13th International Conference on Mining Software Repositories
              May 2016
              544 pages
              ISBN:9781450341868
              DOI:10.1145/2901739

              Copyright © 2016 ACM

              Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 14 May 2016

              Permissions

              Request permissions about this article.

              Request Permissions

              Check for updates

              Qualifiers

              • research-article

              Upcoming Conference

              ICSE 2025

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader