Browse > Article
http://dx.doi.org/10.5762/KAIS.2020.21.11.1

Application of Advertisement Filtering Model and Method for its Performance Improvement  

Park, Raegeun (Department of Smart Information and Telecommunication Engineering, Sangmyung University)
Yun, Hyeok-Jin (Department of Smart Information and Telecommunication Engineering, Sangmyung University)
Shin, Ui-Cheol (Department of Smart Information and Telecommunication Engineering, Sangmyung University)
Ahn, Young-Jin (Department of Smart Information and Telecommunication Engineering, Sangmyung University)
Jeong, Seungdo (Department of Smart Information and Telecommunication Engineering, Sangmyung University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.21, no.11, 2020 , pp. 1-8 More about this Journal
Abstract
In recent years, due to the exponential increase in internet data, many fields such as deep learning have developed, but side effects generated as commercial advertisements, such as viral marketing, have been discovered. This not only damages the essence of the internet for sharing high-quality information, but also causes problems that increase users' search times to acquire high-quality information. In this study, we define advertisement as "a text that obscures the essence of information transmission" and we propose a model for filtering information according to that definition. The proposed model consists of advertisement filtering and advertisement filtering performance improvement and is designed to continuously improve performance. We collected data for filtering advertisements and learned document classification using KorBERT. Experiments were conducted to verify the performance of this model. For data combining five topics, accuracy and precision were 89.2% and 84.3%, respectively. High performance was confirmed, even if atypical characteristics of advertisements are considered. This approach is expected to reduce wasted time and fatigue in searching for information, because our model effectively delivers high-quality information to users through a process of determining and filtering advertisement paragraphs.
Keywords
Advertisement; Unstructured Data; Filtering; Natural Language Processing; KorBERT;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
연도 인용수 순위
1 Anonymous. Driver's insurance cost in the 20s [Internet]. NAVER, [cited Jun 13 2020], Available From: https://bit.ly/2OaM31W (accessed Jun. 24, 2020)
2 Hyoung-Woong Yoon, Namsu Kim, Sohyun Park, Ye-Bin Jeong, Hae-Yeoun Lee, "SNS Advertisement Filtering System", Proceedings of KIIT Conference, pp.261-262, Jun. 2018.
3 Ji-A. Kim, Geum-Boon. Lee, "An Effective Method for Blocking Illegal Sports Gambling Ads on Social Media", Journal of the Korea Society of Computer and Information, Vol.24, No.12, pp.201-207, Dec. 2019. DOI : https://doi.org/10.9708/jksci.2019.24.12.201   DOI
4 Taewon Song, Heeok Lee, "A Study of Legal Aspect on the Blocking Ads by AdBlock", InHa Law Review, Vol.19, No.2, pp.147-175, 2016.
5 Sunju Park, Seungwha Chung, Naseong Pyo, Soonki Hwang, "Bottlenecks in Building an Online Customer Base: A Experimental Field Study on Viral Marketing", The Journal of the Korea Contents Association, Vol.19, No.1, pp.682-695, 2019. DOI : http://dx.doi.org/10.5392/JKCA.2019.19.01.682   DOI
6 Jeil Oh, "Supreme Court of Korea 'Portal screen change software release, No business interruption' " [Internet], NEWSIS, cited May. 05 2016, Available From: https://bit.ly/2VcIZWR (accessed Jun. 23, 2020)
7 Sangheum Hwang, Dohyun Kim, "BERT-based Classification Model for Korean Documents", The Journal of Society for e-Business Studies, Vol.25, No.1, pp.203-214, Feb. 2020. DOI : http://dx.doi.org/10.7838/jsebs.2020.25.1.203   DOI
8 ITU(International Telecommunication Union), Individuals using the Internet, 2005-2019 [Internet], Available From: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx (accessed Jun 25, 2020)
9 David Reinsel, John Gantz, John Rydning, The Digitization of the World From Edge to Core, IDC(International Data Corporation), pp.1-28, Nov.s.
10 Devlin, Jacob, et al. "Bert: Pre-training of deep bidirectional transformers for language understanding" Association for Computational Linguistics, pp.4171-4186, Jun. 2019. DOI : http://dx.doi.org/10.18653/v1/N19-1423   DOI
11 Korea Industrial Marketing Institute, "Viral Marketing", Marketing, Vol.49, No.5, pp.59-67, May. 2015.
12 ETRI, Results of KorBERT language model comparison with Google [Internet], ETRI, Available From: http://aiopen.etri.re.kr/service_dataset.php (accessed Jun. 24, 2020)
13 DMC MEDIA, 2019 Portal Site Usage Behavior Survey Analysis Report - Summary, Analysis Report, DMC REPORT, Korea, pp.1-13
14 Soojong Lim, Hyunki Kim, "Current Status of Deep Learning Pre-training for Natural Language Processing and Application to Korean" Institute of Culture Convergence Archiving, Vol.2, No.2, pp.111-118, Oct. 2019
15 JeongHoon Lee, "Online A dvertising by Search Engine and Criminal Responsibility", Hongik Law Review, Vol.19, No.4, pp.59-81, 2018. DOI: http://dx.doi.org/10.16960/jhlr.19.1.201802.59   DOI
16 Byunghee Kim, "New Definitions and Ranges on Advertising : Mixed Methods Approach", KJA(The Korean Journal of Advertising), Vol.24, No.2, pp.225-254, 2013.