A framework for information synthesis into sentiment indicators using text mining methods

Sequential pattern mining methods have been found to be applicable in a large number of domains. Sequential data is omnipresent. Sequential pattern mining methods have been used to analyze this data and identify patterns. Such patterns have been used to implement efficient systems that can recommend based on previously observed patterns, help in making predictions, improve usability of systems, detect events, and in general help in making strategic product decisions. In this chapter, we discuss the applications of sequential data mining in a variety of domains like healthcare, education, Web usage mining, text mining, bioinformatics, telecommunications, intrusion detection, et cetera. We conclude with a summary of the work.

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Conclusion

Computational Text Analysis ◽

10.1093/oso/9780198567400.003.0018 ◽

2006 ◽

Author(s):

Soumya Raychaudhuri

Keyword(s):

Text Mining ◽

High Throughput ◽

Relevant Literature ◽

Critical Role ◽

The Body ◽

Data Sets ◽

Data Set ◽

New Techniques ◽

Scientific Text ◽

Mining Methods

The genomics era has presented many new high throughput experimental modalities that are capable of producing large amounts of data on comprehensive sets of genes. In time there will certainly be many more new techniques that explore new avenues in biology. In any case, textual analysis will be an important aspect of the analysis. The body of the peer-reviewed scientific text represents all of our accomplishments in biology, and it plays a critical role in hypothesizing and interpreting any data set. To altogether ignore it is tantamount to reinventing the wheel with each analysis. The volume of relevant literature approaches proportions where it is all but impossible to manually search through all of it. Instead we must often rely on automated text mining methods to access the literature efficiently and effectively. The methods we present in this book provide an introduction to the avenues that one can employ to include text in a meaningful way in the analysis of these functional genomics data sets. They serve as a complement to the statistical methods such as classification and clustering that are commonly employed to analyze data sets. We are hopeful that this book will serve to encourage the reader to utilize and further develop text mining in their own analyses.

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Text mining analysis roadmap (TMAR) for service research

Journal of Services Marketing ◽

10.1108/jsm-02-2019-0074 ◽

2020 ◽

Vol 34 (1) ◽

pp. 30-47 ◽

Cited By ~ 2

Author(s):

Mohamed Zaki ◽

Janet R. McColl-Kennedy

Keyword(s):

Machine Learning ◽

Text Mining ◽

Design Methodology ◽

Data Sources ◽

Theory Testing ◽

Service Research ◽

Data Types ◽

Content Type ◽

Research And Practice ◽

Mining Methods

Purpose The purpose of this paper is to offer a step-by-step text mining analysis roadmap (TMAR) for service researchers. The paper provides guidance on how to choose between alternative tools, using illustrative examples from a range of business contexts. Design/methodology/approach The authors provide a six-stage TMAR on how to use text mining methods in practice. At each stage, the authors provide a guiding question, articulate the aim, identify a range of methods and demonstrate how machine learning and linguistic techniques can be used in practice with illustrative examples drawn from business, from an array of data types, services and contexts. Findings At each of the six stages, this paper demonstrates useful insights that result from the text mining techniques to provide an in-depth understanding of the phenomenon and actionable insights for research and practice. Originality/value There is little research to guide scholars and practitioners on how to gain insights from the extensive “big data” that arises from the different data sources. In a first, this paper addresses this important gap highlighting the advantages of using text mining to gain useful insights for theory testing and practice in different service contexts.

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A Research on the Transmission and Variation of Tales to Build Korean Yadam Computer Aided Digital Archive

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.307.502 ◽

2013 ◽

Vol 307 ◽

pp. 502-505 ◽

Cited By ~ 1

Author(s):

Woon Ho Choi ◽

Dong Keon Kim

Keyword(s):

Text Mining ◽

Pairwise Comparison ◽

Digital Archive ◽

Minor Variation ◽

Computer Aided ◽

Mining Methods

In this paper, the transmission and variation of tales between Yadamjip's was investigated. Yadamjip is a collection of Yadam, which is a tale of unofficial histories. The data was compiled from 12 books of Yadamjip and the number of tales used in this research is 2,144. The pairwise comparison of 2,144 tales to each other was committed and the transmission and variation of Yadamjip is inferred by computational clustering and text mining methods from the similarity of tales in each Yadamjip. Among the 12 Yadamjip's., it is revealed that there are three major categories of Yadamjip only with respect to the transmission relation. Especially, GIMUN (NL), GIMUM (YS), HAEDONG, CHEONGGU, DONGPAE, GYESEO were revealed to share various tales with trivial or minor variation.

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