The Literature Review of Text Data Mining

Yanli Xu; Rong Zhao

doi:doi:10.11648/j.sd.20170506.18

| Peer-Reviewed

The Literature Review of Text Data Mining

Yanli Xu, Rong Zhao

Published in Science Discovery (Volume 5, Issue 6)

Received: 16 November 2017 Published: 21 November 2017

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

At present, the study of structured data analysis, researchers at home and abroad mainly focus on the learners in the network teaching environment, with diversified interactive learning mode, text based nonstructured data is generated continuously. In recent years, through the mining of text data to evaluate the learner's ability and knowledge of psychology and screening the behavior has become a new learning method. Firstly introduces the concept and technology of text data mining, then introduces the tools and methods of text mining in the mainstream, finally expounds the present situation of the application of text mining technology in natural and Social Sciences in the two fields and 6 application analysis, namely curriculum evaluation support learners, knowledge and ability, learning community groups, learning behavior of crisis early warning, forecasting learning effect and learning state visualization.

Published in	Science Discovery (Volume 5, Issue 6)
DOI	10.11648/j.sd.20170506.18
Page(s)	438-443
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Text Data Mining, Analysis Tools, Learning Analysis

References

[1]	李尚昊，朝乐门.文本挖掘在中文信息分析中的应用研究述评[J].情报科学，2016,(08):153-159。
[2]	W. W. Cohen. What can we learn from the web? In proceedings of the Sixteenth International Conference on Machine Learning (ICML’99), 1999, 515-521.
[3]	Pons-Porrata A, Berlanga-Llavori R, Ruiz-Shulcloper J. Topic discovery based on text mining techniques[J]. Informa⁃tion Processing & Manmanagement,2007, 43(3): 752-768.
[4]	袁军鹏，朱东华，李毅，李连宏，黄进.文本挖掘技术研究进展[J].计算机应用研究，2006,(02):1-4。
[5]	钱峰.国内数据挖掘工具研究综述[J].情报杂志，2008,(10):11-13。
[6]	王敏，李海存，许培扬.国外专利文本挖掘可视化工具研究[J].图书情报工作，2009,(24):86-90。
[7]	蔡溢，杨洋，殷红梅.基于ROST文本挖掘软件的贵阳市城市旅游品牌受众感知研究[J].重庆师范大学学报(自然科学版)，2015,(01):126-134。
[8]	范并思.社会科学信息分析中的文本挖掘[J].图书情报工作，2012,56(8):6-9。
[9]	李尚昊，朝乐门.文本挖掘在中文信息分析中的应用研究述评[J].情报科学，2016,(08):153-159.
[10]	魏桂英，高学东，武森.基于领域本体的个性化文本信息检索[J].辽宁工程技术大学学报(自然科学版)，2011,(02):316-320.
[11]	刘三女牙，彭晛，刘智，孙建文，刘林，郑年亨.基于文本挖掘的学习分析应用研究[J].电化教育研究，2016,(02):23-30.
[12]	Leong, C-K., ee, -H., ak, -K. Mining Sentiments in SMS Texts for Teaching Evaluation [J]. Expert Systems with Applications, 2012, 39(3): 2584~2589.
[13]	Kontogiannis, Valsamidis Kazanidis et al. Course Opinion Mining Methodology for Knowledge Discovery, Based on WebSocial Media [A]. Proceedings of the 18th Panhellenic Conference on Informatics [C]. New York: ACM Press, 2014: 1~6.
[14]	Bravo –Marquez, L’Huillier, Moya, et al. An Automatic Text Comprehension Classifier Based on Mental Models and Latent Semantic Features [A]. Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies [C]. New York: ACM Press, 2011: 158~162.
[15]	Kardan, Conati, A Framework for Capturing Distinguishing User Interaction Behaviors in Novel Interfaces [A]. EDM [C]. New York: ACM Press, 2011: 159~168.
[16]	L. J. Austin. How to Do Things with Words [M]. Oxford: Oxford University Press, 1962.
[17]	C Hsu. L., Chou, W., Chang,. H.. Edu Miner: Using Text Mining for Automatic Formative Assessment [J]. Expert Systems with Applications, 2011, 38(4): 3431~3439.

Cite This Article

Plain Text BibTeX RIS

APA Style

Yanli Xu, Rong Zhao. (2017). The Literature Review of Text Data Mining. Science Discovery, 5(6), 438-443. https://doi.org/10.11648/j.sd.20170506.18

Copy | Download

ACS Style

Yanli Xu; Rong Zhao. The Literature Review of Text Data Mining. Sci. Discov. 2017, 5(6), 438-443. doi: 10.11648/j.sd.20170506.18

Copy | Download

AMA Style

Yanli Xu, Rong Zhao. The Literature Review of Text Data Mining. Sci Discov. 2017;5(6):438-443. doi: 10.11648/j.sd.20170506.18

Copy | Download

@article{10.11648/j.sd.20170506.18,
  author = {Yanli Xu and Rong Zhao},
  title = {The Literature Review of Text Data Mining},
  journal = {Science Discovery},
  volume = {5},
  number = {6},
  pages = {438-443},
  doi = {10.11648/j.sd.20170506.18},
  url = {https://doi.org/10.11648/j.sd.20170506.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20170506.18},
  abstract = {At present, the study of structured data analysis, researchers at home and abroad mainly focus on the learners in the network teaching environment, with diversified interactive learning mode, text based nonstructured data is generated continuously. In recent years, through the mining of text data to evaluate the learner's ability and knowledge of psychology and screening the behavior has become a new learning method. Firstly introduces the concept and technology of text data mining, then introduces the tools and methods of text mining in the mainstream, finally expounds the present situation of the application of text mining technology in natural and Social Sciences in the two fields and 6 application analysis, namely curriculum evaluation support learners, knowledge and ability, learning community groups, learning behavior of crisis early warning, forecasting learning effect and learning state visualization.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - The Literature Review of Text Data Mining
AU  - Yanli Xu
AU  - Rong Zhao
Y1  - 2017/11/21
PY  - 2017
N1  - https://doi.org/10.11648/j.sd.20170506.18
DO  - 10.11648/j.sd.20170506.18
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 438
EP  - 443
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20170506.18
AB  - At present, the study of structured data analysis, researchers at home and abroad mainly focus on the learners in the network teaching environment, with diversified interactive learning mode, text based nonstructured data is generated continuously. In recent years, through the mining of text data to evaluate the learner's ability and knowledge of psychology and screening the behavior has become a new learning method. Firstly introduces the concept and technology of text data mining, then introduces the tools and methods of text mining in the mainstream, finally expounds the present situation of the application of text mining technology in natural and Social Sciences in the two fields and 6 application analysis, namely curriculum evaluation support learners, knowledge and ability, learning community groups, learning behavior of crisis early warning, forecasting learning effect and learning state visualization.
VL  - 5
IS  - 6
ER  -

Copy | Download

Author Information

Yanli Xu

School of Educational Technology Information, Central China Normal University, Wuhan, China
Rong Zhao

School of Educational Technology Information, Central China Normal University, Wuhan, China

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Yanli Xu, Rong Zhao. (2017). The Literature Review of Text Data Mining. Science Discovery, 5(6), 438-443. https://doi.org/10.11648/j.sd.20170506.18

Copy | Download

ACS Style

Yanli Xu; Rong Zhao. The Literature Review of Text Data Mining. Sci. Discov. 2017, 5(6), 438-443. doi: 10.11648/j.sd.20170506.18

Copy | Download

AMA Style

Yanli Xu, Rong Zhao. The Literature Review of Text Data Mining. Sci Discov. 2017;5(6):438-443. doi: 10.11648/j.sd.20170506.18

Copy | Download

@article{10.11648/j.sd.20170506.18,
  author = {Yanli Xu and Rong Zhao},
  title = {The Literature Review of Text Data Mining},
  journal = {Science Discovery},
  volume = {5},
  number = {6},
  pages = {438-443},
  doi = {10.11648/j.sd.20170506.18},
  url = {https://doi.org/10.11648/j.sd.20170506.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20170506.18},
  abstract = {At present, the study of structured data analysis, researchers at home and abroad mainly focus on the learners in the network teaching environment, with diversified interactive learning mode, text based nonstructured data is generated continuously. In recent years, through the mining of text data to evaluate the learner's ability and knowledge of psychology and screening the behavior has become a new learning method. Firstly introduces the concept and technology of text data mining, then introduces the tools and methods of text mining in the mainstream, finally expounds the present situation of the application of text mining technology in natural and Social Sciences in the two fields and 6 application analysis, namely curriculum evaluation support learners, knowledge and ability, learning community groups, learning behavior of crisis early warning, forecasting learning effect and learning state visualization.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - The Literature Review of Text Data Mining
AU  - Yanli Xu
AU  - Rong Zhao
Y1  - 2017/11/21
PY  - 2017
N1  - https://doi.org/10.11648/j.sd.20170506.18
DO  - 10.11648/j.sd.20170506.18
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 438
EP  - 443
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20170506.18
AB  - At present, the study of structured data analysis, researchers at home and abroad mainly focus on the learners in the network teaching environment, with diversified interactive learning mode, text based nonstructured data is generated continuously. In recent years, through the mining of text data to evaluate the learner's ability and knowledge of psychology and screening the behavior has become a new learning method. Firstly introduces the concept and technology of text data mining, then introduces the tools and methods of text mining in the mainstream, finally expounds the present situation of the application of text mining technology in natural and Social Sciences in the two fields and 6 application analysis, namely curriculum evaluation support learners, knowledge and ability, learning community groups, learning behavior of crisis early warning, forecasting learning effect and learning state visualization.
VL  - 5
IS  - 6
ER  -

Copy | Download