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Hybrid method in revealing facts behind texts: A combination of text mining and qualitative approach: (Sub-theme: Exploring New Research Methods for Public Administration: beyond positivism)

机译:杂交方法在文本背后揭示事实:文本挖掘与定性方法的组合:(子主题:探索公共行政的新研究方法:超越实证主义)

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Social media has become one of the primary sources of data which available for policy analysts and policymakers. As the evidence, active Twitter users are sending 500 million tweets per day containing thoughts, opinions, pictures, and other information. Social media offers new challenges related to how the data is acquired and how to analyze it. Unfortunately, the state-of-the-art methods in text mining are still unable to interpret texts fully. Thus, in social media analysis, we can only make a conclusion based on the insight into an event. Therefore, we propose a hybrid method that combines text mining and qualitative methods for analyzing social media data. This research was composed based on a review of studies and experimental results on the data taken from the Twitter. The results show that both techniques can complement each other and give in-depth analysis of the data. Furthermore, the results can be employed to observe social media data in a faster, cheaper, and more precise way. More importantly, the results of this study serve as a basis for further development of a method to reveal the facts behind texts that obtained from social media.
机译:社交媒体已成为可用于政策分析师和政策制定者的主要数据来源之一。作为证据,活动推特用户每天发送5亿推文,包含思想,意见,图片和其他信息。社交媒体提供了与如何获取数据的新相关挑战以及如何分析它。不幸的是,文本挖掘中最先进的方法仍然无法完全解释文本。因此,在社交媒体分析中,我们只能基于洞察事件的洞察力结束。因此,我们提出了一种混合方法,该方法结合了文本挖掘和定性方法来分析社交媒体数据。该研究基于对从Twitter所采取的数据的研究和实验结果进行审查。结果表明,两种技术都可以相互补充并对数据进行深入分析。此外,可以采用结果以更快,更便宜,更精确地观察社交媒体数据。更重要的是,本研究的结果是进一步发展一种揭示从社交媒体获得的文本背后事实的方法的基础。

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