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A Review of Two Text-Mining Packages: SAS TextMining and WordStat

机译:两种文本挖掘程序包的回顾:SAS TextMining和WordStat

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摘要

The purpose of this article is to review two text mining packages, namely, WordStat and SAS TextMiner. WordStat is developed by Provalis Research. SAS TextMiner is a product of SAS. We review the features offered by each package on each of the following key steps in analyzing unstructured data: (1) data preparation, including importing and cleaning; (2) performing association analysis; and (3) presenting the findings, including illustrative quotes and graphs. We also evaluate each package on its ability to help researchers extract major themes from a dataset. Both packages offer a variety of features that effectively help researchers run associations and present results. However, in extracting themes from unstructured data, both packages were only marginally helpful. The researcher still needs to read the data and make all the difficult decisions. This finding stems from the fact that the software can search only for specific terms in documents or categorize documents based on common terms. Respondents, however, may use the same term or combination of terms to mean different things. This implies that a text mining approach, which is based on analysis units other than terms, may be more powerful in extracting themes, an idea we touch upon in the conclusion section.
机译:本文的目的是回顾两个文本挖掘程序包,即WordStat和SAS TextMiner。 WordStat由Provalis Research开发。 SAS TextMiner是SAS的产品。我们将在分析非结构化数据的以下每个关键步骤中回顾每个软件包提供的功能:(1)数据准备,包括导入和清理; (2)进行关联分析; (3)提出调查结果,包括说明性的引用和图表。我们还将评估每个软件包的功能,以帮助研究人员从数据集中提取主要主题。这两个软件包都提供了多种功能,可以有效地帮助研究人员进行关联并呈现结果。但是,在从非结构化数据中提取主题时,这两个软件包都只能提供很少的帮助。研究人员仍然需要读取数据并做出所有困难的决定。该发现源于以下事实:该软件只能搜索文档中的特定术语或基于通用术语对文档进行分类。但是,受访者可能使用相同的术语或术语组合来表示不同的含义。这意味着基于文本而非术语的分析单元的文本挖掘方法在提取主题方面可能会更强大,这是我们在结论部分中谈到的一种想法。

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