This paper describes how corpus-assisted discourse analysis based on keyword (KW) identification and interpretation can benefit from employing Market basket analysis (MBA) after KW extraction. MBA is a data mining technique used originally in marketing that can reveal consistent associations between items in a shopping cart, but also between keywords in a corpus of many texts. By identifying recurring associations between KWs we can compensate for the lack of wider context which is a major issue impeding the interpretation of isolated KWs (esp. when analyzing large data). To showcase the advantages of MBA in "re-contextualizing" keywords within the discourse, a pilot study on the topic of migration was conducted contrasting anti-system and center-right Czech internet media. was conducted. The results show that MBA is useful in identifying the dominant strategy of anti-system news portals: to weave in a confounding ideological undercurrent and connect the concept of migrants to a multitude of other topics (i.e., flooding the discourse).
翻译:本文介绍在KW提取后,利用市场篮子分析(MBA)对主题的识别和解释如何能从基于关键词(KW)的识别和解释的系统辅助话语分析中受益。MBA最初是一种用于营销的数据挖掘技术,它能够揭示购物车各项目之间的一致性,但也揭示许多文本中各关键词之间的关联。通过确定KWs之间反复出现的关联,我们可以弥补缺乏更广泛的背景,这是一个妨碍解释孤立 KWs的一个主要问题(在分析大数据时,缓存)。为了在讨论中展示MBA在“重新翻版”关键词中的优势,开展了一项关于移民主题的试点研究,对反系统和中右捷克互联网媒体进行了对比。研究结果表明,MBA有助于确定反系统新闻门户的主导战略:在意识形态下编织,并将移民的概念与其他许多专题(即充斥着话语)联系起来。