结合数据挖掘和文本挖掘的数据分析支持

Tomoya Matsumoto, W. Sunayama, Y. Hatanaka, K. Ogohara
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引用次数: 18

摘要

近年来,数据挖掘和文本挖掘技术被频繁地用于分析问卷和评论数据。关联分析和聚类分析等数据挖掘技术用于市场分析,因为它们可以发现隐藏在大量数字数据中的关系和规则。另一方面,文本挖掘技术,如关键词提取和意见提取用于问卷或评论文本分析,因为这些可以支持我们在文本数据中调查消费者意见。然而,数据挖掘工具和文本挖掘工具不能在一个环境中使用。因此,同时具有数字和文本数据的数据不能很好地分析,因为数字部分和文本部分不能连接起来进行解释。本文提出了一种可以同时处理数字和文本数据的挖掘框架。我们可以在独特的框架中使用数字和文本分析工具迭代数据收缩和数据分析。实验结果表明,该系统可有效地用于评论文本的数据分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Analysis Support by Combining Data Mining and Text Mining
In recent years, data mining and text mining techniques have been frequently used for analyzing questionnaire and review data. Data mining techniques such as association analysis and cluster analysis are used for marketing analysis, because those can discover relationships and rules hiding in enormous numerical data. On the other hand, text mining techniques such as keywords extraction and opinion extraction are used for questionnaire or review text analysis, because those can support us to investigate consumers opinion in text data. However, data mining tools and text mining tools cannot be used in a single environment. Therefore, a data which has both numerical and text data is not well analyzed because the numerical part and text part cannot be connected for interpretation.In this paper, a mining framework that can treat both numerical and text data is proposed. We can iterate data shrink and data analysis with both numerical and text analysis tools in the unique framework. Based on experimental results, the proposed system was effectively used to data analysis for review texts.
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