Review of Survey research in Fuzzy approach for text mining

Yi Wei Lai, Mu Yen Chen

研究成果: Article同行評審


Text mining has been common research field, with the emergence of Web 2.0 and the development of social software, the amount of text generated every day has increased dramatically. The texts contain a lot of valuable information, how to analysis for the information from text is very important. Therefore, many research have explored related methods and various fields of text mining, such as sentiment analysis, text clustering, text summarization, etc., However, unlike other numerical data that can be directly calculated in terms of character performance, the calculation must be performed after vector conversion, and the words may have polysemy, which provides more challenges for natural language processing. Due to the above challenges, various techniques are used for data preprocessing before analysis. In addition to the common statistical and discrete methods for text data, methods based on fuzzy logic provide another option for effective natural language analysis, Therefore, in recent years, more and more studies have added Fuzzy logic to additionally capture the context semantics of individual words to help more accurate natural language processing. This survey research discusses multiple text mining methods, The survey paper discusses multiple text mining methods, subfields and application fields, covering the literature published between 2010 and 2022, It is organized to the subtasks to be performed, the methods and natural language processing techniques used, and the application scenarios. At the end of this study, the research has provided the key point discussion and relevant suggestions for text mining combined with fuzzy logic.

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期刊IEEE Access
出版狀態Accepted/In press - 2023

All Science Journal Classification (ASJC) codes

  • 電腦科學(全部)
  • 材料科學(全部)
  • 工程 (全部)
  • 電氣與電子工程


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