@article{ART002198153},
author={KIM, Yoonsung and Ho-Chang Lee and Lee, Seok Kee and Lee, Do-Gil and Hangook Kim and You-Eil Kim},
title={A Study on Industry Information Analysis Methodology Based on Text Mining: PEST and Polarity Analysis Using Sentence Classification},
journal={Journal of Knowledge Information Technology and Systems},
issn={1975-7700},
year={2017},
volume={12},
number={1},
pages={109-122},
doi={10.34163/jkits.2017.12.1.011}
TY - JOUR
AU - KIM, Yoonsung
AU - Ho-Chang Lee
AU - Lee, Seok Kee
AU - Lee, Do-Gil
AU - Hangook Kim
AU - You-Eil Kim
TI - A Study on Industry Information Analysis Methodology Based on Text Mining: PEST and Polarity Analysis Using Sentence Classification
JO - Journal of Knowledge Information Technology and Systems
PY - 2017
VL - 12
IS - 1
PB - Korea Knowledge Information Technology Society
SP - 109
EP - 122
SN - 1975-7700
AB - Today’s companies are in an environment where they have to survive in ever-increasing competition in the industry, by constantly identifying changes and trends in their industries and by periodically reflecting them in their policies and product development. For this purpose, one of the tasks that should be carried out is the analysis of industrial information. Most companies acquire industry analytical information at the cost of a large amount of time, manpower or, with the help of external professional analysts. However, since this conventional method is a somewhat heuristic and qualitative approach. The quality of these analysis results are different each time. A huge amount of industry related information is produced online in real time and when the information is reflected in the analysis as much as possible, it is required to introduce a new analytical method. In this paper, we propose a text mining methodology that extracts information from large amount of source data and automatically classifies it into each category of industry analysis framework. By constructing a sentence classifier using feature selection technique based on machine learning method, information that can be classified by indicators of universally used industry analysis framework is collected in sentence form. We performed PEST and polarity analysis by using our system and evaluated the classification accuracy of the proposed system through experiments.
KW - Industrial analysis;PEST;SWOT;Polarity analysis;Text mining;Machine learning
DO - 10.34163/jkits.2017.12.1.011
ER -
KIM, Yoonsung, Ho-Chang Lee, Lee, Seok Kee, Lee, Do-Gil, Hangook Kim and You-Eil Kim. (2017). A Study on Industry Information Analysis Methodology Based on Text Mining: PEST and Polarity Analysis Using Sentence Classification. Journal of Knowledge Information Technology and Systems, 12(1), 109-122.
KIM, Yoonsung, Ho-Chang Lee, Lee, Seok Kee, Lee, Do-Gil, Hangook Kim and You-Eil Kim. 2017, "A Study on Industry Information Analysis Methodology Based on Text Mining: PEST and Polarity Analysis Using Sentence Classification", Journal of Knowledge Information Technology and Systems, vol.12, no.1 pp.109-122. Available from: doi:10.34163/jkits.2017.12.1.011
KIM, Yoonsung, Ho-Chang Lee, Lee, Seok Kee, Lee, Do-Gil, Hangook Kim, You-Eil Kim "A Study on Industry Information Analysis Methodology Based on Text Mining: PEST and Polarity Analysis Using Sentence Classification" Journal of Knowledge Information Technology and Systems 12.1 pp.109-122 (2017) : 109.
KIM, Yoonsung, Ho-Chang Lee, Lee, Seok Kee, Lee, Do-Gil, Hangook Kim, You-Eil Kim. A Study on Industry Information Analysis Methodology Based on Text Mining: PEST and Polarity Analysis Using Sentence Classification. 2017; 12(1), 109-122. Available from: doi:10.34163/jkits.2017.12.1.011
KIM, Yoonsung, Ho-Chang Lee, Lee, Seok Kee, Lee, Do-Gil, Hangook Kim and You-Eil Kim. "A Study on Industry Information Analysis Methodology Based on Text Mining: PEST and Polarity Analysis Using Sentence Classification" Journal of Knowledge Information Technology and Systems 12, no.1 (2017) : 109-122.doi: 10.34163/jkits.2017.12.1.011
KIM, Yoonsung; Ho-Chang Lee; Lee, Seok Kee; Lee, Do-Gil; Hangook Kim; You-Eil Kim. A Study on Industry Information Analysis Methodology Based on Text Mining: PEST and Polarity Analysis Using Sentence Classification. Journal of Knowledge Information Technology and Systems, 12(1), 109-122. doi: 10.34163/jkits.2017.12.1.011
KIM, Yoonsung; Ho-Chang Lee; Lee, Seok Kee; Lee, Do-Gil; Hangook Kim; You-Eil Kim. A Study on Industry Information Analysis Methodology Based on Text Mining: PEST and Polarity Analysis Using Sentence Classification. Journal of Knowledge Information Technology and Systems. 2017; 12(1) 109-122. doi: 10.34163/jkits.2017.12.1.011
KIM, Yoonsung, Ho-Chang Lee, Lee, Seok Kee, Lee, Do-Gil, Hangook Kim, You-Eil Kim. A Study on Industry Information Analysis Methodology Based on Text Mining: PEST and Polarity Analysis Using Sentence Classification. 2017; 12(1), 109-122. Available from: doi:10.34163/jkits.2017.12.1.011
KIM, Yoonsung, Ho-Chang Lee, Lee, Seok Kee, Lee, Do-Gil, Hangook Kim and You-Eil Kim. "A Study on Industry Information Analysis Methodology Based on Text Mining: PEST and Polarity Analysis Using Sentence Classification" Journal of Knowledge Information Technology and Systems 12, no.1 (2017) : 109-122.doi: 10.34163/jkits.2017.12.1.011