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Requirements Engineering

, Volume 22, Issue 3, pp 387–412 | Cite as

An exploratory study of Twitter messages about software applications

  • Emitza GuzmanEmail author
  • Rana Alkadhi
  • Norbert Seyff
RE 2016

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.

Keywords

Requirements engineering Software evolution User feedback Content analysis Text mining 

Notes

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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Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  1. 1.University of ZurichZurichSwitzerland
  2. 2.Technische Universität MünchenGarchingGermany
  3. 3.FHNWWindischSwitzerland

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