Browse > Article
http://dx.doi.org/10.14400/JDC.2020.18.9.017

Application of Sentiment Analysis and Topic Modeling on Rural Solar PV Issues : Comparison of News Articles and Blog Posts  

Ki, Jaehong (Graduate School of Environmental Studies, Seoul National University)
Ahn, Seunghyeok (Environmental Planning Institute, Seoul National University)
Publication Information
Journal of Digital Convergence / v.18, no.9, 2020 , pp. 17-27 More about this Journal
Abstract
News articles and blog posts have influence on social agenda setting and this study applied text mining on the subject of solar PV in rural area appeared in those media. Texts are gained from online news articles and blog posts with rural solar PV as a keyword by web scrapping, and these are analysed by sentiment analysis and topic modeling technique. Sentiment analysis shows that the proportion of negative texts are significantly lower in blog posts compared to news articles. Result of topic modeling shows that topics related to government policy have the largest loading in positive articles whereas various topics are relatively evenly distributed in negative articles. For blog posts, topics related to rural area installation and environmental damage are have the largest loading in positive and negative texts, respectively. This research reveals issues related to rural solar PV by combining sentiment analysis and topic modeling that were separately applied in previous studies.
Keywords
Rural solar PV; News Articles; Blog Posts; Sentiment Analysis; Topic Modeling;
Citations & Related Records
Times Cited By KSCI : 18  (Citation Analysis)
연도 인용수 순위
1 D. Sarkar. (2019). Text Analytics with Python: A Practitioner's Guide to Natural Language Processing. Berkeley, CA : Apress. DOI : 10.1007/978-1-4842-4354-1
2 K. A. Kim & J. H. Ku. (2017). A Study on the Change of the View of Love using Text Mining and Sentiment Analysis. Journal of Digital Convergence, 15(2), 285-294. DOI : 10.14400/JDC.2017.15.2.285   DOI
3 B. Burscher, R. Burscher, & C. H. de Vreese. (2016). Frames Beyond Words: Applying Cluster and Sentiment Analysis to News Coverage of the Nuclear Power Issue. Social Science Computer Review, 34(5), 530-545. DOI : 10.1177/0894439315596385   DOI
4 S. Y. Cho & E. P. Hong. (2019). A Study on the Polarity of Apartment Price News Using Big Data Analysis Method. Journal of Digital Convergence, 17(9), 47-54. DOI : 10.14400/JDC.2019.17.9.047
5 K. Sheshadri, N. Ajmeri & J. Staddon. (2017, August). No (Privacy) News is Good News: An Analysis of New York Times and Guardian Privacy News from 2010-2016. 2017 15th Annual Conference on Privacy, Security and Trust (PST). (pp. 159-168). Calgary, AB : IEEE. DOI : 10.1109/PST.2017.00027
6 S. J. Kim, J. E. Kim, W. Y. Seong & Y. H. Kim. (2019). Design of Video Advertisement Analysis via Analysis of Internet Term Sensitivity. Journal of KIISE, 46(9), 919-925. DOI : 10.5626/JOK.2019.46.9.919   DOI
7 Y. H. Lim & H. B. Kim. (2019). A study on the sentiment analysis using big data of hotels online review. Korean Journal of Hospitality and Tourism 28(7), 105-123. DOI : 10.24992/KJHT.2019.10.28.07.105
8 R. Mitchell. (2018). Web Scraping with Python: Collecting More Data from the Modern Web, 2nd Edition. Sebastopol, CA : O'Reilly Media. Retrieved from https://www.oreilly.com/library/view/web-scraping-with/9781491985564/
9 M. L. Park, S. W. Shin, S. D. Oh, & S. H. Kang. (2019). A Study on the Direction of Resident Acceptability for Photovoltaic System in Rural region - A Case of the Rural Village in Munback-Myeon, Jincheon-Gun, Chungbuk. Journal Of The Korean Institute Of Rural Architecture, 21(3), 77-84. DOI : 10.14577/kirua.2019.21.3.77   DOI
10 S. S. Jung. (2017). Study on Measures to Improve Residents' Acceptance for Renewable Energy. Ulsan : Korea Energy Economics Institute. Retrieved from http://www.keei.re.kr/main.nsf/index.html?open&p=%2Fweb_keei%2Fd_results.nsf%2Fmain_all%2F95EDFE7DC198969F492583CB002F18FC&s=%3FOpenDocument%26menucode%3DS0%26category%3D%25EA%25B8%25B0%25EB%25B3%25B8%25EC%2597%25B0%25EA%25B5%25AC
11 S. S. Jung & S. M. Lee. (2018). Study on the Benefit Sharing System to Improve Acceptance for Renewable Energy. Ulsan : Korea Energy Economics Institute. Retrieved from http://www.keei.re.kr/main.nsf/index.html?open&p=%2Fweb_keei%2Fd_results.nsf%2Fmain_all%2F95EDFE7DC198969F492583CB002F18FC&s=%3FOpenDocument%26menucode%3DS0%26category%3D%25EA%25B8%25B0%25EB%25B3%25B8%25EC%2597%25B0%25EA%25B5%25AC
12 B. Tsolmon & G. S. Lee. (2017). Topic Model Reflecting User Behavior and Time Analysis for Extracting Disaster Events from Social Data. Information and Communications Magazine, 34(6), 43-50. Retrieved from http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE07179145
13 Y. J. Kim, S. S. Kim, K. S. Chae, D. S. Seo, J. Y. Park, S. H. Song & S. M. Choo. (2018). Study on Problems and Measures to Improve the Diffusion of Rural Solar Energy. Naju : Korea Rural Economic Institute. Retrieved from http://www.krei.re.kr/krei/researchReportView.do?key=67&pageType=010101&biblioId=519732&pageUnit=10&searchCnd=all&searchKrwd=%EB%86%8D%EC%B4%8C%20%ED%83%9C%EC%96%91%EA%B4%91&pageIndex=1&engView=
14 C. S. Jang & S. K. Kim. (2017). A Study on the Stakeholders Perception Type on the Location of the Solar Light Power Generation Facility: Case of SeoCheon Province. Journal of local government studies, 29(3), 113-133. DOI : 10.9727/jmsk.2016.29.3.113   DOI
15 S. A. Park, S. J. Yun. (2018). Opposition to and Acceptance of Siting Solar Power Facilities from the Place Attachment Viewpoint. ECO 22(2), 267-317. DOI : 10.16974/STLR.2016.22.2.007
16 J. S. Park & J. S. Lee. (2019). An Investigation into the Causal Relationship and the Cross Correlation between Apartment House Sales Prices and Real Estate Online News An Approach to the Sentiment Analysis Using Unstructured Big Data of Online News Articles. Journal of Korea Planning Association 54(1), 131-147. DOI : 10.17208/jkpa.2019.02.54.1.131   DOI
17 S. H. Seo & J. T. Kim. (2016). Deep Learning-Based Sentiment Analysis Research Trend. Journal of Korea Multimedia Society 20(3), 8-22. Retrieved from http://www.dbpia.co.kr.libproxy.snu.ac.kr/journal/articleDetail?nodeId=NODE07053234&language=ko_KR
18 J. H. Choi, H. S. Lee, & E. H. Jin. (2019). A Topic Modeling Analysis of the News Topic on the 4th Industrial Revolution in Korea: Focusing on the Difference by Media Type and Each Major Period. Journal of Cybercommunication Academic Society, 36(2), 173-219. DOI : 10.36494/JCAS.2019.06.36.2.173   DOI
19 S. S. Lee, I. H. Yoo & J. H. Kim. (2020). An analysis of public perception on Artificial Intelligence(AI) education using Big Data: Based on News articles and Twitter. Journal of Digital Convergence, 18(6), 9-16. DOI : 10.14400/JDC.2020.18.6.009   DOI
20 S. H. Noh. (2020). Analysis of Issues Related to Artificial Intelligence Based on Topic Modeling. Journal of Digital Convergence, 18(5), 75-87. DOI : 10.14400/JDC.2020.18.5.075   DOI
21 S. M. Lee & S. G. Hong. (2020). Policy Agenda Proposals from Text Mining Analysis of Patents and News Articles. Journal of Digital Convergence, 18(3), DOI : 10.14400/JDC.2020.18.3.001
22 S. Y. Choi & E. J. Ko. (2019). Analysis of Korean Journal of Journalism & Communication Studies from 1960 to 2018 using Metadata with Dynamic Topic Modeling. Korean Journal of Journalism & Communication Studies, 63(4), 7-42. DOI : 10.20879/kjjcs.2019.63.4.001
23 S. B. Joo. (2019). Sentimental Analysis of Crime News Data-Focused on the Comparison before and after Regulation of Media Report. Korean Criminal Psychology Review, 15, 127-140. Retrieved from http://scholar.dkyobobook.co.kr/searchDetail.laf?barc ode=4010027331317
24 D. G. Lee. (2019). Inter-Media Agenda Setting Between Daily Newspapers and Blogs: Content Analysis of Choi Soon-sil Gate. The Journal of Political Science & Communication, 22(2), 53-90. DOI : 10.35731/kpca.2015..39.003   DOI
25 H. J. Ahn & Y. Ha. (2019). Analysis of the Relationship between the Type of Experience and Blog Texts. The Journal of Korean Institute of Information Technology, 15(2), 131-140. DOI : 10.14801/jkiit.2017.15.2.131
26 S. Kang & Y. Shon. (2020). Study on the Phenomenon of Early Childhood Private Education through Topic Modeling Analysis: Focusing on Domestic Newspaper Articles and Blogs. Journal of Future Early Childhood Education, 27(1), 177-199. DOI : 10.22155/JFECE.27.1.177.199   DOI
27 T. D. Lee, S. Lee, & C. Oh. (2017). A Comparative Analysis of Nuclear Energy Issue Frames in Press Releases and News Articles : A Topic Modeling Approach. Journal of Communication Science, 17(3), 172-229. DOI : 10.14696/jcs.2017.09.17.3.172   DOI
28 G. J. Yoo & E. A. Kim. (2019). A Study on the Morpheme and Emotional Analysis of Newspaper Articles on Children's Right to Play. The Journal of Korea Open Association for Early Childhood Education, 24(5), 109-132. DOI : 10.20437/KOAECE24-5-06   DOI
29 S. Shin & H. Kim. (2019). A Proposal of Research Method for Measuring Marketing Communication Effect: Analysis of Image of "National Fitness Award" Project through LDA-based Topic Modeling. Korean Society For Sport Management, 24(6), 48-62. DOI : 10.31308/KSSM.24.6.4   DOI
30 J. H. Lee, I. S. Lee, K. S. Jung, B. H. Chae, & J. Y. Lee. (2017). Patents and Papers Trends of Solar-Photovoltaic(PV) Technology using LDA Algorithm. Journal of Digital Convergence, 15(9), 231-239. DOI : 10.14400/JDC.2017.15.9.231   DOI
31 MOTIE & KEA. (2019). New & Renewable Energy White Paper. Retrieved from https://www.knrec.or.kr/pds/pds_read.aspx?no=291&searchfield=&searchword=&page=1
32 B. Bengfort, R. Bilbro, & T. Ojeda. (2018). Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning. Sebastopol, CA : O'Reilly Media. Retrieved from https://www.oreilly.com/library/view/applied-text-an alysis/9781491963036/
33 P. Singh. (2018). Machine Learning with PySpark: With Natural Language Processing and Recommender Systems. Berkeley, CA : Apress. DOI : 10.1007/978-1-4842-4131-8
34 J. G. Shin. (2020). Analysis regarding Complaints of Courier Consumers and Workers in the Parcel Delivery Service by using Topic Model. Journal of Convergence for Information Technology, 10(2), 39-48. DOI : 10.22156/CS4SMB.2020.10.02.039   DOI