On negative results when using sentiment analysis tools for software engineering research

R.M. Jongeling, P. Sarkar, S. Datta, A. Serebrenik

Research output: Contribution to journalArticleAcademicpeer-review

116 Citations (Scopus)
224 Downloads (Pure)


Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain. In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets (issue trackers and Stack Overflow questions) and different sentiment analysis tools and observe that the disagreement between the tools can lead to diverging conclusions. Finally, we perform two replications of previously published studies and observe that the results of those studies cannot be confirmed when a different sentiment analysis tool is used.

Original languageEnglish
Pages (from-to)2543-2584
Number of pages42
JournalEmpirical Software Engineering
Issue number5
Early online date2017
Publication statusPublished - 1 Oct 2017


  • Negative results
  • Replication study
  • Sentiment analysis tools


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