Review of Survey Research in Fuzzy Approach for Text Mining

Text mining has been a popular research topic in the field of natural language processing. 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, and how to do the analysis to extract the information from that text is very important. Therefore, many researchers 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 calculated directly in terms of character performance, the calculation must be performed after vector conversion, and the words may have polysemy, which provides more challenges for text mining. Given 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 text analysis, Therefore, in recent years, more and more studies have added fuzzy logic to additionally capture the context semantics of individual words to improve the accuracy of natural language processing. This current survey research discusses multiple text mining methods, subfields, and application fields, covering the literature published between 2010 and 2022. It is organized based on the subtasks to be performed, the methods and text mining techniques used, and the application scenarios. At the end of this study, the key points will be discussed, and relevant suggestions for future research on text mining combined with fuzzy logic will be made.

Automated summary

This article discusses the research focus and technological development of text mining, introducing various methods and application fields. It emphasizes the challenges in text mining and the importance of data preprocessing, as well as the role of fuzzy logic in improving accuracy.