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An exploratory study of Twitter messages about software applications
Requirements Engineering ( IF 2.1 ) Pub Date : 2017-07-15 , DOI: 10.1007/s00766-017-0274-x
Emitza Guzman , Rana Alkadhi , Norbert Seyff

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.

中文翻译:

关于软件应用程序的 Twitter 消息的探索性研究

Twitter 微博平台的用户每天通过短信分享大量信息。其中一些所谓的推文讨论了与软件相关的问题,并且可能包含与开发这些应用程序的公司相关的信息。此类推文有可能帮助需求工程师更好地了解用户需求,从而为软件演化提供重要信息。然而,人们对讨论软件相关问题的推文的性质知之甚少。在本文中,我们报告了有关软件应用程序的推文的使用特征、内容和自动分类潜力。我们的结果基于一项探索性研究,其中我们使用描述性统计、内容分析、机器学习和词汇情感分析来探索 10 个数据集,986,495 条推文涉及 30 种不同的软件应用程序。我们的结果表明,在大量推文中搜索有关软件应用程序的相关信息可以比作大海捞针。然而,这些相关信息可以为软件公司提供有价值的输入,并支持这些推文中讨论的应用程序的不断发展。此外,我们的结果表明,可以使用机器学习和词汇情感分析技术自动提取有关推文的相关性、作者和情感极性的信息。这些相关信息可以为软件公司提供有价值的输入,并支持这些推文中讨论的应用程序的不断发展。此外,我们的结果表明,可以使用机器学习和词汇情感分析技术自动提取有关推文的相关性、作者和情感极性的信息。这些相关信息可以为软件公司提供有价值的输入,并支持这些推文中讨论的应用程序的不断发展。此外,我们的结果表明,可以使用机器学习和词汇情感分析技术自动提取有关推文的相关性、作者和情感极性的信息。
更新日期:2017-07-15
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