An analysis of affective expressions in articles of popular science by text mining

Kuei Chen Chiu, Chun Lin Liu, Ruey Lin Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper aims to analyze affective expressions in articles of popular science by text mining with the keywords Cancer and Immunity. This study selects 145 articles from the website of a magazine and segmented them into 410,919 terms. And the study uses an automatic system to classify the terms into vocabulary categories, selecting the affective terms with specific vocabulary categories. The results show those the affective terms in the analyzed articles of popular science are not significantly differential with year and decade. But there is a significantly negative correlation with the quantity of articles that are published in the same year. That is, the more articles are published the less proportion of affective terms to the summary terms occurs on the articles.

Original languageEnglish
Title of host publicationIEEM 2015 - 2015 IEEE International Conference on Industrial Engineering and Engineering Management
PublisherIEEE Computer Society
Pages966-970
Number of pages5
ISBN (Electronic)9781467380669
DOIs
Publication statusPublished - 2016 Jan 18
EventIEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2015 - Singapore, Singapore
Duration: 2015 Dec 62015 Dec 9

Publication series

NameIEEE International Conference on Industrial Engineering and Engineering Management
Volume2016-January
ISSN (Print)2157-3611
ISSN (Electronic)2157-362X

Conference

ConferenceIEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2015
CountrySingapore
CitySingapore
Period15-12-0615-12-09

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting (miscellaneous)
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

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