Competition component identification on twitter

Cheng Huang Yang, Ji De Chen, Hung-Yu Kao

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

Abstract

Twitter becomes a popular microblogging platform that let people to express their opinions on the web in recent years. Companies with new products always want to find consumers or public opinions about their products and services after they released new products. Due to this reason, more and more researchers use the opinion mining technology on this microblog media that contains abundant and real-time information, to extract useful opinions and information. In this paper, we aim to mine the opinions on Twitter and further extract the competition relations discussed on Twitter. For Example, if we want to know how people express their opinion about “Packer” (an American football team name), we also want to know what the Packer’s competitors are. In this paper, we introduce a hashtag graph and use the ranks in this graph to represent the competition behavior and competition components (competitors).

Original languageEnglish
Title of host publicationTrends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops
Subtitle of host publicationDANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers
EditorsWen-Chih Peng, Haixun Wang, Zhi-Hua Zhou, Tu Bao Ho, Vincent S. Tseng, Arbee L.P. Chen, James Bailey
PublisherSpringer Verlag
Pages584-595
Number of pages12
ISBN (Electronic)9783319131856
DOIs
Publication statusPublished - 2014 Jan 1
EventInternational Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan
Duration: 2014 May 132014 May 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8643
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014
CountryTaiwan
CityTainan
Period14-05-1314-05-16

Fingerprint

Packers
Express
Opinion Mining
Graph in graph theory
Real-time
Industry

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Yang, C. H., Chen, J. D., & Kao, H-Y. (2014). Competition component identification on twitter. In W-C. Peng, H. Wang, Z-H. Zhou, T. B. Ho, V. S. Tseng, A. L. P. Chen, & J. Bailey (Eds.), Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers (pp. 584-595). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8643). Springer Verlag. https://doi.org/10.1007/978-3-319-13186-3_52
Yang, Cheng Huang ; Chen, Ji De ; Kao, Hung-Yu. / Competition component identification on twitter. Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers. editor / Wen-Chih Peng ; Haixun Wang ; Zhi-Hua Zhou ; Tu Bao Ho ; Vincent S. Tseng ; Arbee L.P. Chen ; James Bailey. Springer Verlag, 2014. pp. 584-595 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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title = "Competition component identification on twitter",
abstract = "Twitter becomes a popular microblogging platform that let people to express their opinions on the web in recent years. Companies with new products always want to find consumers or public opinions about their products and services after they released new products. Due to this reason, more and more researchers use the opinion mining technology on this microblog media that contains abundant and real-time information, to extract useful opinions and information. In this paper, we aim to mine the opinions on Twitter and further extract the competition relations discussed on Twitter. For Example, if we want to know how people express their opinion about “Packer” (an American football team name), we also want to know what the Packer’s competitors are. In this paper, we introduce a hashtag graph and use the ranks in this graph to represent the competition behavior and competition components (competitors).",
author = "Yang, {Cheng Huang} and Chen, {Ji De} and Hung-Yu Kao",
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Yang, CH, Chen, JD & Kao, H-Y 2014, Competition component identification on twitter. in W-C Peng, H Wang, Z-H Zhou, TB Ho, VS Tseng, ALP Chen & J Bailey (eds), Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8643, Springer Verlag, pp. 584-595, International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014, Tainan, Taiwan, 14-05-13. https://doi.org/10.1007/978-3-319-13186-3_52

Competition component identification on twitter. / Yang, Cheng Huang; Chen, Ji De; Kao, Hung-Yu.

Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers. ed. / Wen-Chih Peng; Haixun Wang; Zhi-Hua Zhou; Tu Bao Ho; Vincent S. Tseng; Arbee L.P. Chen; James Bailey. Springer Verlag, 2014. p. 584-595 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8643).

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

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AU - Yang, Cheng Huang

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AU - Kao, Hung-Yu

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N2 - Twitter becomes a popular microblogging platform that let people to express their opinions on the web in recent years. Companies with new products always want to find consumers or public opinions about their products and services after they released new products. Due to this reason, more and more researchers use the opinion mining technology on this microblog media that contains abundant and real-time information, to extract useful opinions and information. In this paper, we aim to mine the opinions on Twitter and further extract the competition relations discussed on Twitter. For Example, if we want to know how people express their opinion about “Packer” (an American football team name), we also want to know what the Packer’s competitors are. In this paper, we introduce a hashtag graph and use the ranks in this graph to represent the competition behavior and competition components (competitors).

AB - Twitter becomes a popular microblogging platform that let people to express their opinions on the web in recent years. Companies with new products always want to find consumers or public opinions about their products and services after they released new products. Due to this reason, more and more researchers use the opinion mining technology on this microblog media that contains abundant and real-time information, to extract useful opinions and information. In this paper, we aim to mine the opinions on Twitter and further extract the competition relations discussed on Twitter. For Example, if we want to know how people express their opinion about “Packer” (an American football team name), we also want to know what the Packer’s competitors are. In this paper, we introduce a hashtag graph and use the ranks in this graph to represent the competition behavior and competition components (competitors).

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M3 - Conference contribution

AN - SCOPUS:84915816844

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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PB - Springer Verlag

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Yang CH, Chen JD, Kao H-Y. Competition component identification on twitter. In Peng W-C, Wang H, Zhou Z-H, Ho TB, Tseng VS, Chen ALP, Bailey J, editors, Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers. Springer Verlag. 2014. p. 584-595. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-13186-3_52