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An exploratory study of Twitter messages about software applications

  • RE 2016
  • Published:
Requirements Engineering Aims and scope Submit manuscript

Abstract

Users of the Twitter microblogging platform share a considerable amount of information through short messages on a daily basis. Some of these so-called tweets discuss issues related to software and could include information that is relevant to the companies developing these applications. Such tweets have the potential to help requirements engineers better understand user needs and therefore provide important information for software evolution. However, little is known about the nature of tweets discussing software-related issues. In this paper, we report on the usage characteristics, content and automatic classification potential of tweets about software applications. Our results are based on an exploratory study in which we used descriptive statistics, content analysis, machine learning and lexical sentiment analysis to explore a dataset of 10,986,495 tweets about 30 different software applications. Our results show that searching for relevant information on software applications within the vast stream of tweets can be compared to looking for a needle in a haystack. However, this relevant information can provide valuable input for software companies and support the continuous evolution of the applications discussed in these tweets. Furthermore, our results show that it is possible to use machine learning and lexical sentiment analysis techniques to automatically extract information about the tweets regarding their relevance, authors and sentiment polarity.

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Notes

  1. http://www.apple.com/itunes/charts/, https://play.google.com/store/apps/top, http://www.amazon.com/best-sellers-software/zgbs/software.

  2. http://www.tweepy.org/.

  3. https://dev.Twitter.com/rest/public/search.

  4. The search query is: tweepy.Cursor (api.search, q\(\,=\,\)’APP_NAME -filter:retweets’, lang\(\,=\,\)’en’), where APP_NAME is the name of the software application. We ran the query iteratively (every 7–9 days) for each software application in our dataset.

  5. We consider tweets that do not belong to the unrelated, unclear or noise categories as fulfilling these criteria. A definition of each category can be found in Table 3.

  6. We follow Twitter’s suit and count each link as 23 characters and do not consider media and photographs for the count.

  7. Previous research [35] found that 75% of the re-tweets occur less than a day after the concerned tweet has been posted. Thus, we consider that the number of re-tweets and likes is in most cases complete due to them being collected after the tweet has been present for at most 7–9 days (because of the Twitter API restrictions described in Sect. 2.2).

  8. This includes all different hashtag combinations of the software application name. For example, for Adobe Photoshop: #AdobePhotoshop, #Adobe #Photoshop, etc.

  9. Windows 7 and Windows 10 share the common Twitter account @Windows.

  10. We do not count the categories unrelated, unclear, noise and other in this final count.

  11. We performed stratification during our tenfold cross-validation.

  12. http://meka.sourceforge.net, default configuration. Details in "Appendix".

  13. The name that uniquely identifies the user in Twitter; it appears after @ sign (e.g., @twitterapi).

  14. http://tapbots.com/tweetbot/mac/.

  15. Referred as friends by Twitter.

  16. We performed stratification during our tenfold cross-validation.

  17. http://www.cs.waikato.ac.nz/ml/weka, default configuration. Details in "Appendix".

  18. http://stackoverflow.com/

  19. The Android and Windows stores allow for bidirectional communication. However, the Apple and Blackberry stores do not.

  20. http://bit.ly/2kZtPzl.

  21. http://weka.sourceforge.net/doc.dev/overview-summary.html.

  22. http://meka.sourceforge.net/api-1.7/index.html.

References

  1. Twitter API Rate Limits. https://dev.twitter.com/rest/public/rate-limiting. Accessed 04 April 2017

  2. Achananuparp P, Lubis IN, Tian Y, Lo D, Lim EP (2012) Observatory of Trends in Software Related Microblogs. In: Proceedings of the 27th IEEE/ACM international conference on automated software engineering, ASE’12, pp 334–337

  3. Almaatouq A, Alabdulkareem A, Nouh M, Shmueli E, Alsaleh M, Singh VK, Alarifi A, Alfaris A, Pentland AS (2014) Twitter: Who Gets Caught? Observed trends in social micro-blogging Spam. In: Proceedings of the 2014 ACM conference on Web Science, WebSci’14, pp 33–41

  4. Almaatouq A, Shmueli E, Nouh M, Alabdulkareem A, Singh VK, Alsaleh M, Alarifi A, Alfaris A, Pentland AS (2016) If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts. Int J Inf Secur 15(5):475–491

    Article  Google Scholar 

  5. Alsaleh M, Alarifi A, Al-Salman AM, Alfayez M, Almuhaysin A (2014) TSD: detecting sybil accounts in Twitter. In: Proceedings of the 13th international conference on machine learning and applications, pp 463–469

  6. Asur S, Huberman BA (2010) Predicting the future with social media. Proc IEEE/WIC/ACM Int Conf Web Intell Intell Agent Technol 1:492–499

    Google Scholar 

  7. Benevenuto F, Magno G, Rodrigues T, Almeida V (2010) Detecting spammers on Twitter. In: Proceedings of collaboration, electronic messaging, anti-abuse and spam conference, CEAS’10

  8. Berry DM (2017) Evaluation of tools for hairy requirements engineering and software engineering tasks. In: Technical report, University of Waterloo

  9. Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1–8

    Article  Google Scholar 

  10. Bougie G, Starke J, Storey MA, German DM (2011) Towards understanding Twitter use in software engineering: preliminary findings, ongoing challenges and future questions. In: Proceedings of the 2nd international workshop on Web 2.0 for software engineering, Web2SE’11. ACM, pp 31–36

  11. Chen N, Lin J, Hoi SC, Xiao X, Zhang B (2014) AR-miner: mining informative reviews for developers from mobile app marketplace. In: Proceedings of the 36th international conference on software engineering, ICSE’14, pp 767–778

  12. Cheng Z, Caverlee J, Lee K (2010) You are where you Tweet: a content-based approach to geo-locating Twitter users. In: Proceedings of the 19th ACM international conference on information and knowledge management, CIKM’10, pp 759–768

  13. Chu Z, Gianvecchio S, Wang H, Jajodia S (2012) Detecting automation of Twitter accounts: Are you a human, bot, or cyborg? IEEE Trans Dependable Secure Comput 9(6):811–824

    Article  Google Scholar 

  14. Davis CA, Varol O, Ferrara E, Flammini A, Menczer F (2016) BotOrNot: a system to evaluate social bots. In: Proceedings of the 25th international conference companion on world wide web, WWW’16 Companion, pp 273–274

  15. De Choudhury M, Counts S (2013) Understanding affect in the workplace via social media. In: Proceedings of the 2013 conference on computer supported cooperative work, CSCW’13, pp 303–316

  16. Di Sorbo A, Panichella S, Alexandru CV, Shimagaki J, Visaggio CA, Canfora G, Gall HC (2016) What would users change in my app? Summarizing app reviews for recommending software changes. In: Proceedings of the international symposium on foundations of software engineering, pp 499–510

  17. Dietterich IG (2000) Ensemble methods in machine learning. In: Proceedings of the international workshop on multiple classifier systems, pp 1–15

  18. Galvis Carreño LV, Winbladh K (2013) Analysis of user comments: an approach for software requirements evolution. In: Proceedings of the 2013 international conference on software engineering, ICSE’13, pp 582–591

  19. Grier C, Thomas K, Paxson V, Zhang M (2010) @Spam: the underground on 140 characters or less. In: Proceedings of the 17th ACM conference on computer and communications security, CCS’10, pp 27–37

  20. Groen EC, Doerr J, Adam S (2015) Towards crowd-based requirements engineering a research preview. In: Requirements engineering: foundation for software quality. Springer, pp 247–253.

  21. Gu X, Kim S (2015) “What Parts of Your Apps are Loved by Users?” (T). In: Proceedings of the 2015 30th IEEE/ACM international conference on automated software engineering, ASE’15, pp 760–770

  22. Guzman E, Alkadhi R, Seyff N (2016) A needle in a haystack: What do Twitter users say about software? In: Proceedings of the international conference on requirements engineering, RE’16

  23. Guzman E, Aly O, Bruegge B (2015) Retrieving diverse opinions from app reviews. In: Proceedings of the 2015 ACM/IEEE international symposium on empirical software engineering and measurement, ESEM’15, pp 1–10

  24. Guzman E, Azócar D, Li Y (2014) Sentiment analysis of commit comments in GitHub: an empirical study. In: Proceedings of the 11th working conference on mining software repositories, MSR’14, pp 352–355. ACM, 2014

  25. Guzman E, Bruegge B (2013) Towards emotional awareness in software development teams. In: Proceedings of the 2013 9th joint meeting on foundations of software engineering, ESEC/FSE’13, pp 671–674

  26. Guzman E, El-Haliby M, Bruegge B (2015) Ensemble methods for app review classification: an approach for software evolution (N). In: Proceedings of the 2015 30th IEEE/ACM international conference on automated software engineering, ASE’15, pp 771–776

  27. Guzman E, Maalej W (2014) How do users like this feature? A fine grained sentiment analysis of app reviews. In: Proceedings of the 2014 IEEE 22nd international requirements engineering conference, RE’14, pp 153–162

  28. Hong L, Dan O, Davison BD (2011) Predicting popular messages in Twitter. In: Proceedings of the 20th international conference companion on world wide web, WWW ’11, pp 57–58

  29. Hoon L, Vasa R, Schneider JG, Grundy J et al (2013) An analysis of the mobile app review landscape: trends and implications. Faculty of information and communication technologies, Swinburne University of Technology, Technical report

  30. Iacob C, Harrison R (2013) Retrieving and analyzing mobile apps feature requests from online reviews. In: Proceedings of the working conference on mining software repositories, MSR’13, pp 41–44

  31. Jansen BJ, Zhang M, Sobel K, Chowdury A (2009) Twitter power: Tweets as electronic word of mouth. J Am Soc Inf Sci Technol 60(11):2169–2188

    Article  Google Scholar 

  32. Johann T, Maalej W (2015) Democratic mass participation of users in requirements engineering? In: Proceedings of the 2015 IEEE 23rd international requirements engineering conference, RE’15, pp 256–261

  33. Kucuktunc O, Cambazoglu BB, Weber I, Ferhatosmanoglu H (2012) A large-scale sentiment analysis for Yahoo! answers. In: Proceedings of the 5th ACM international conference on web search and data mining, WSDM ’12, pp 633–642

  34. Kukreja N, Boehm B (2012) Process implications of social networking-based requirements negotiation tools. In: Proceedings of the international conference on software and system process, ICSSP’12, pp 68–72

  35. Kwak H, Lee C, Park H, Moon S (2010) What is Twitter, a social network or a news media? In: Proceedings of the 19th international conference on world wide web, WWW’10, pp 591–600

  36. Lim SL, Damian D, Finkelstein A (2011) StakeSource2. 0: using social networks of stakeholders to identify and prioritise requirements. In: Proceedings of the 33rd international conference on software engineering, ICSE’11, pp 1022–1024

  37. Lim SL, Finkelstein A (2012) StakeRare: using social networks and collaborative filtering for large-scale requirements elicitation. IEEE Trans Softw Eng 38(3):707–735

    Article  Google Scholar 

  38. Lim SL, Quercia D, Finkelstein A (2010) StakeNet: using social networks to analyse the stakeholders of large-scale software projects. In: Proceedings of the 32Nd ACM/IEEE international conference on software engineering, ICSE’10, pp 295–304

  39. Luaces O, Díez J, Barranquero J, del Coz JJ, Bahamonde A (2012) Binary relevance efficacy for multilabel classification. Prog Artif Intell 1(4):303–313

    Article  Google Scholar 

  40. Maalej W, Nabil H (2015) Bug report, feature request, or simply praise? On automatically classifying app reviews. In: Proceedings of the IEEE 23rd international requirements engineering conference, RE’15, pp 116–125

  41. Martin W, Sarro F, Jia Y, Zhang Y, Harman M (2016) A survey of app store analysis for software engineering. In: IEEE transactions on software engineering

  42. Martinez-Romo J, Araujo L (2013) Detecting malicious Tweets in trending topics using a statistical analysis of language. Expert Syst Appl 40(8):2992–3000

    Article  Google Scholar 

  43. McCord M, Chuah M (2011) Spam detection on Twitter using traditional classifiers. In: Proceedings of the 8th international conference on autonomic and trusted computing, ATC’11, pp 175–186

  44. Mitchell TM (1997) Machine Learning, volume 4 of McGraw-Hill Series in Computer Science. McGraw-Hill, New York

    Google Scholar 

  45. Neuendorf K (2002) The content analysis guidebook. Sage Publications, Thousand Oaks

    Google Scholar 

  46. Novielli N, Calefato F, Lanubile F (2014) Towards discovering the role of emotions in stack overflow. In: Proceedings of the 6th international workshop on social software engineering, SSE’14, pp 33–36

  47. Ortu M, Adams B, Destefanis G, Tourani P, Marchesi M, Tonelli R (2015) Are bullies more productive? Empirical study of affectiveness vs. issue fixing time. In: Proceedings of the 12th working conference on mining software repositories, MSR’15, pp 303–313

  48. Pagano D, Bruegge B (2013) User involvement in software evolution practice : a case study. In: Proceedings of the international conference on software engineering, ICSE ’13, 2013

  49. Pagano D, Maalej W (2013) User feedback in the appstore: an empirical study. In: Proceedings of the 21st IEEE international requirements engineering conference, RE’13, pp 125–134

  50. Palomba F, Linares-Vásquez M, Bavota G, Oliveto R, Di Penta M, Poshyvanyk D, De Lucia A (2015) User reviews matter! tracking crowdsourced reviews to support evolution of successful apps. In: Proceedings of the IEEE international conference on software maintenance and evolution, ICSME’15, pp 291–300

  51. Panichella S, Di Sorbo A, Guzman E, Visaggio C, Canfora G, Gall H (2015) How can i improve my app? Classifying user reviews for software maintenance and evolution. In: Proceedings of the 31st international conference on software maintenance and evolution, ICSME’15, pp 281–290

  52. Pennebaker J, Chung C, Ireland M (2007) The development and psychological properties of LIWC2007

  53. Pfitzner R, Garas A, Schweitzer E (2012) Emotional divergence influences information spreading in Twitter. In: Proceedings of the 6th international AAAI conference on weblogs and social media, ICWSM’12

  54. Prasetyo PK, Lo D, Achananuparp P, Tian Y, Lim EP (2012) Automatic classification of software related microblogs. In: Proceedings of the 28th IEEE international conference on software maintenance, ICSM’12, pp 596–599

  55. Seyff N, Ollmann G, Bortenschlager M (2014) AppEcho: a user-driven, in situ feedback approach for mobile platforms and applications. In: Proceedings of the 1st international conference on mobile software engineering and systems, MOBILESoft’14, pp 99–108

  56. Seyff N, Todoran I, Caluser K, Singer L, Glinz M (2015) Using popular social network sites to support requirements elicitation, prioritization and negotiation. J Internet Serv Appl 6(1):1–16

    Article  Google Scholar 

  57. Sharma A, Tian Y, Lo D (2015) NIRMAL: automatic identification of software relevant Tweets leveraging language model. In: Proceedings of the IEEE 22nd international conference on software analysis, evolution and reengineering, SANER’15, pp 449–458

  58. Sharma A, Tian Y, Lo D (2015) What’s hot in software engineering Twitter space? In: Proceedings of the IEEE international conference on software maintenance and evolution, ICSME’15. IEEE, pp 541–545

  59. Singer L, Figueira Filho F, Storey MA (2014) Software engineering at the speed of light: how developers stay current using Twitter. In: Proceedings of the 36th international conference on software engineering, ICSE’14, pp 211–221

  60. Singh M, Bansal D, Sofat S (2014) Detecting malicious users in Twitter using classifiers. In: Proceedings of the 7th international conference on security of information and networks, SIN’14, pp 247–253

  61. Sriram B, Fuhry D, Demir E, Ferhatosmanoglu H, Demirbas M (2010) Short text classification in Twitter to improve information filtering. In: Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval, SIGIR’10, pp 841–842

  62. Stieglitz S, Dang-Xuan L (2013) Emotions and information diffusion in social media—sentiment of microblogs and sharing behavior. J Manag Inf Syst 29(4):217–248

    Article  Google Scholar 

  63. Suh B, Hong L, Pirolli P, Chi EH (2010) Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network. In: Proceedings of the IEEE 2nd international conference on social computing, SocialCom’10, pp 177–184

  64. Thelwall M, Buckley K, Paltoglou G (2011) Sentiment in Twitter events. J Am Soc Inf Sci Technol 62(2):406–418

    Article  Google Scholar 

  65. Thelwall M, Buckley K, Paltoglou G (2012) Sentiment strength detection for the social web. J Am Soc Inf Sci Technol 63(1):163–173

    Article  Google Scholar 

  66. Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A (2010) Sentiment strength detection in short informal text. J Am Soc Inf Sci Technol 61(12):2544–2558

    Article  Google Scholar 

  67. Thomas K, Grier C, Song D, Paxson V (2011) Suspended accounts in retrospect: an analysis of Twitter spam. In: Proceedings of the 2011 ACM SIGCOMM conference on internet measurement conference, IMC’11, pp 243–258

  68. Tian Y, Achananuparp P, Lubis IN, Lo D, Lim EP (2012) What does software engineering community microblog about? In: Proceedings of the 9th IEEE working conference on mining software repositories, MSR’12, pp 247–250

  69. Tian Y, Lo D (2014) An exploratory study on software microblogger behaviors. In: Proceedings of the IEEE 4th workshop on mining unstructured data, MUD’14. IEEE, pp 1–5

  70. Tsoumakas G, Katakis I (2007) Multi-label classification: an overview. Int J Data Warehous Min 3:1–13

    Article  Google Scholar 

  71. Tumasjan A, Sprenger T, Sandner P, Welpe I (2010) Predicting elections with Twitter: What 140 characters reveal about political sentiment. In: Proceedings of the 4th international AAAI conference on weblogs and social media, ICWSM’10, pp 178–185

  72. Villarroel L, Bavota G, Russo B, Oliveto R, Di Penta M (2016) Release planning of mobile apps based on user reviews. In: Proceedings of the 38th international conference on software engineering, ICSE’16, pp 14–24

  73. Wehrmaker T, Gärtner S, Schneider K (2012) Contexter feedback system. In: Proceedings of the 34th international conference on software engineering, ICSE’12, pp 1459–1460

  74. Wilson T, Hoffmann P, Somasundaran S, Kessler J, Wiebe J, Choi Y, Cardie C, Riloff E, Patwardhan S (2005) OpinionFinder: a system for subjectivity analysis. In: Proceedings of HLT/EMNLP on interactive demonstrations, HLT-Demo’05, pp 34–35

  75. Yang T, Lee D, Yan S (2013) Steeler Nation, 12th man, and boo birds: classifying Twitter user interests using time series. In: Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining, ASONAM’13, pp 684–691

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Acknowledgements

We thank Martin Glinz, Dustin Wüest, Melanie Stade, Bernd Brügge, Eya Ben Charrada, Kim Lauenroth, Marjo Kauppinen and Fabiano Dalpiaz for their feedback and discussions. This work was partially supported by the European Commission within the SUPERSEDE project (ID 644018) and a PhD scholarship provided by King Saud University for the second author.

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Authors and Affiliations

  1. University of Zurich, Zurich, Switzerland

    Emitza Guzman & Norbert Seyff

  2. Technische Universität München, Garching, Germany

    Rana Alkadhi

  3. FHNW, Windisch, Switzerland

    Norbert Seyff

Authors
  1. Emitza Guzman
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  2. Rana Alkadhi
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  3. Norbert Seyff
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Corresponding author

Correspondence to Emitza Guzman.

Appendix

Appendix

1.1 Weka configurations

In our work, we applied the default configurations of the classification algorithms as set by Weka.Footnote 21

1.1.1 Naive Bayes

weka.classifiers.bayes.NaiveBayes Class for a Naive Bayes classifier using estimator classes.

  • NumDecimalPlaces \(\,=\,\)2. The number of decimal places to be used for the output of numbers in the model.

  • DoNotCheckCapabilities \(\,=\,\)False. If set, the classifier capabilities are not checked before the classifier is built.

  • UsekernelEstimator \(\,=\,\) False. If set, kernel density estimator is used rather than normal distribution for numeric attributes.

  • UseSupervisedDiscretization \(\,=\,\)False. If set, supervised discretization is to be used to convert numeric attributes to nominal ones.

1.1.2 Multinomial Naive Bayes

weka.classifiers.bayes.NaiveBayesMultinomial: Class for building and using a multinomial Naive Bayes classifier.

  • NumDecimalPlaces \(\,=\,\)2. The number of decimal places to be used for the output of numbers in the model.

  • DoNotCheckCapabilities \(\,=\,\)False. If set, the classifier capabilities are not checked before the classifier is built.

1.1.3 SVM

weka.classifiers.functions.SMO: Class that implements John Platt’s sequential minimal optimization algorithm for training a support vector classifier.

  • BuildCalibrationModels \(\,=\,\) False. This option is used to fit calibration models to the outputs of the support vector machine.

  • C \(\,=\,\)1. The complexity constant.

  • Epsilon \(=1.0e-12\). The epsilon for round-off error.

  • FilterType \(\,=\,\) Normalize training data.

  • Kernel \(\,=\,\)The kernel to use. weka.classifiers.functions. supportVector.PolyKernel. The polynomial kernel: K(x, y) = \(\langle x, y \rangle ^p\,or\,K(x, y)\) = \((\langle x, y \rangle +1)^p\) with exponent\(\,=\,\) 1.

  • RandomSeed \(\,=\,\)1. The random number seed for the cross-validation.

  • toleranceParameter \(\,=\,\)0.001. The tolerance parameter.

  • NumDecimalPlaces \(\,=\,\)2. The number of decimal places to be used for the output of numbers in the model.

  • DoNotCheckCapabilities \(\,=\,\)False. If set, the classifier capabilities are not checked before the classifier is built.

1.1.4 J48

weka.classifiers.functions.J48: Class for generating a pruned or unpruned C4.5 decision tree.

  • Binarysplits \(\,=\,\) False. If set, binary splits are used on nominal attributes when building the trees.

  • CollapseTree \(\,=\,\)True. If set, parts are removed that do not reduce training error.

  • ConfidenceFactor \(\,=\,\)0.25. The confidence threshold for pruning.

  • DoNotMakeSplitPointActualValue \(\,=\,\) False. If set, the true point is not relocated to an actual data value.

  • minNumObj \(\,=\,\)2. The minimum number of instances per leaf.

  • NumDecimalPlaces \(\,=\,\)2. The number of decimal places to be used for the output of numbers in the model.

  • NumFolds \(\,=\,\)3. The number of folds for reduced error pruning. One fold is used as pruning set.

  • reducedErrorPruning \(\,=\,\)False. If set, reduced error pruning is used instead of C.

  • SubtreeRaising \(\,=\,\) True. If set, subtree raising is used when pruning.

  • Unpruned \(\,=\,\)False. If set, pruning is performed.

  • UseLaplace \(\,=\,\)False. If set, Laplace smoothing is used for predicted probabilities.

  • UseMDLcorrection \(\,=\,\)True. If set, MDL correction is used when finding splits on numeric attributes.

1.1.5 Random forest

weka.classifiers.trees.RandomForest: Class for constructing a forest of random trees.

  • BagSizePercent \(\,=\,\)100. Size of each bag, as a percentage of the training set size.

  • BreakTiesRandomly \(\,=\,\)False. If set, break ties randomly when several attributes look equally good.

  • CalcOutOfBag \(\,=\,\)False. Whether to calculate the out-of-bag error.

  • MaxDepth \(\,=\,\)0. The maximum depth of the tree, 0 for unlimited.

  • NumDecimalPlaces \(\,=\,\)2. The number of decimal places to be used for the output of numbers in the model.

  • NumExecutionSlots \(\,=\,\)1. Number of execution slots.

  • NumFeatures \(\,=\,\)0. The number of features used in random selection.

  • NumIterations \(\,=\,\)100. The number of iterations to be performed.

  • Seed \(\,=\,\)1. The random number seed to be used.

1.2 Meka configurations

In our work, we applied the default configurations of the multi-label classification methods as set by Meka.Footnote 22 For the base classifiers, we applied the same setting as in Weka.

1.2.1 Binary relevance method

meka.classifiers.multilabel.BR: Class for implementing binary relevance method.

1.2.2 Label powerset method

meka.classifiers.multilabel.LP: Class for implementing label powerset (LP) method.

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Guzman, E., Alkadhi, R. & Seyff, N. An exploratory study of Twitter messages about software applications. Requirements Eng 22, 387–412 (2017). https://doi.org/10.1007/s00766-017-0274-x

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