题名

臺灣網路論壇關注之檔案事件主題及其情感分析

并列篇名

Public Opinion Mining and Sentiment Analysis for Archive Issues on Internet Forums in Taiwan

DOI

10.30177/JLISR.202306_17(2).0003

作者

林巧敏(Chiao-Min Lin);李育賢(Yu-Hsien Li)

关键词

檔案社會議題 ; 社群媒體 ; 文本探勘 ; 文本情感分析 ; 主題探勘 ; Archives of Social Issues ; Social Media ; Text Mining ; Text Sentiment Analysis ; Topic Mining

期刊名称

圖書資訊學研究

卷期/出版年月

17卷2期(2023 / 06 / 01)

页次

71 - 107

内容语文

繁體中文;英文

中文摘要

大眾的意見經常是公務機關提供服務的參考依據,本研究借助數位工具,探討網路論壇中有關檔案議題的貼文,以巨觀及微觀的視角對其內容呈現的主題、情感、脈絡、關聯加以剖析。本研究以四家網路論壇為資料來源,蒐集2012~2021年間,共586篇檔案主題貼文,進行文本預處理後,使用WEKA、CORPRO、CVAW4.0及Gephi數位工具,依序進行主題分析、語料庫分析、情感分析及社會網絡分析。結果顯示,網路論壇討論的檔案議題以「檔案解密公開」為最多;從檔案相關關鍵字的高共現詞彙可觀察出近十年關注的檔案主題,與政治事件高度相關;網路論壇的檔案貼文內容偏負面情感,但在「檔案推廣應用」主題,則是正面高於負面,加入時間分析亦可觀察情感的轉折變化。

英文摘要

The opinions of the public are often the reference for public authorities to provide services. This study uses digital tools to explore the posts on archives issues in online forums, and analyzes and discusses the trend of public opinion in online forums from the perspectives of macroscopic and microscopic on themes, emotions, contexts, and associations presented in their content. Four online forums were taken as data sources to collect a total of 586 posts from 2012 to 2021. After text preprocessing, WEKA, CORPRO, CVAW4.0 and Gephi were conducted in sequence for theme analysis, corpus analysis, sentiment analysis and social network analysis. The results indicated that "archive decryption and disclosure" is the most discussed topic in online forums. According to the high co-occurrence keywords in archives online forum posts, the public opinion of archives in the past ten years can be observed, which is highly correlated with political events. The content of archive posts on online forums is slightly negative, and public opinion is more positive than negative for "archive promotion and access". Adding time analysis can also detect changes in public opinion.

主题分类 人文學 > 圖書資訊學
社會科學 > 傳播學
参考文献
  1. 王貿, M.(2020)。公務人員關注議題之文字探勘:以PTT公職板為例。調查研究—方法與應用,45,119-154。
    連結:
  2. 王毓莉, Y.-L.(2007)。網路論壇與國家機器的碰撞:從三個新聞事件看大陸網路論壇對公共性的實踐。新聞學研究,92,37-95。
    連結:
  3. 邵軒磊, H.,曾元顯, Y.-H.(2018)。文字探勘技術輔助主題分析—以「中國大陸研究」期刊為例。問題與研究,57(1),29-62。
    連結:
  4. 張奕萱, Y.-X.,林巧敏, C.-M.(2022)。運用數位人文工具探討檔案時事議論主題及其情感分類之實作。圖資與檔案學刊,14(2),164-190。
    連結:
  5. 郭文平, W.-P.(2020)。語料庫輔助的媒體論述分析:以臺灣平面媒體中國夢報導為語料的實證研究。資訊社會研究,38,51-92。
    連結:
  6. 陳育正, Y.-C.,孫懋嘉, M.-C.,顧志文, C.-W.,林立偉, L.-W.(2020)。由社群媒體的觀點論習近平主政後對臺統戰策略對我國民眾的影響:以對臺31項措施為例。中國大陸研究,63(2),111-150。
    連結:
  7. 劉嘉薇, J.-W.(2017)。網路統獨的聲量研究:大數據的分析。政治科學論叢,71,113-165。
    連結:
  8. Al-Daihani, S. M.,Abrahams, A.(2018).Analysis of academic libraries’ Facebook posts: Text and data analytics.The Journal of Academic Librarianship,44(2),216-225.
  9. Blake, C.(2011).Text mining.Annual Review of Information Science and Technology,45(1),121-155.
  10. Dave, K.,Lawrence, S.,Pennock, D. M.(2003).Mining the peanut gallery: Opinion extraction and semantic classification of product reviews.Proceedings of the 12th International Conference on World Wide Web,New York, NY:
  11. Dwianto, R.,Nurmandi, A.,Salahudin, S.(2021).The sentiments analysis of Donald Trump and Jokowi’s Twitters on COVID-19 policy dissemination.Webology,18(1),389-405.
  12. Feldman, R.,Sanger, J.(2007).The text mining handbook: Advanced approaches in analyzing unstructured data.Cambridge, UK:Cambridge University Press.
  13. Grimmer, J.,Stewart, B. M.(2013).Text as data: The promise and pitfalls of automatic content analysis methods for political texts.Political Analysis,21(3),267-297.
  14. Hall, M.,Frank, E.,Holmes, G.,Pfahringer, B.,Reutemann, P.,Witten, I. H.(2009).The WEKA data mining software: An update.ACM SIGKDD Explorations Newsletter,11(1),10-18.
  15. Huang, Y.-C.,Lin, C.-M.(2020).The data mining and sentiment analysis of the archival news.11th International Conference of Digital Achieves and Digital Humanities (DADH 2020),Taipei, Taiwan:
  16. Lamba, M.,Madhusudhan, M.(2018).Application of sentiment analysis in libraries to provide temporal information service: A case study on various facets of productivity.Social Network Analysis and Mining,8(1),63.
  17. Lee, G. T.,Kim, C. O.,Song, M.(2020).Semisupervised sentiment analysis method for online text reviews.Journal of Information Science,47(3),387-403.
  18. Lopatovska, I.,Arapakis, I.(2011).Theories, methods and current research on emotions in library and information science, information retrieval and human–computer interaction.Information Processing & Management,47(4),575-592.
  19. Na, J.-C.,Thet, T. T.,Nasution, A. H.,Hassan, F. M.(2011).A sentiment-based digital library of movie review documents using Fedora.Canadian Journal of Information and Library Science,35(3),307-337.
  20. Palomino, M. A.,Aider, F.(2022).Evaluating the effectiveness of text pre-processing in sentiment analysis.Applied Sciences,12(17),8765.
  21. Papachristopoulos, L.,Tsakonas, G.(2020).More than a feeling: Insights and information from a sentiment analysis study.Liber Quarterly: The Journal of the Association of European Research Libraries,30(1),1-12.
  22. Park, S.,Bier, L. M.,Park, H. W.(2021).The effects of infotainment on public reaction to North Korea using hybrid text mining: Content analysis, machine learning-based sentiment analysis, and co-word analysis.Profesional De La Información,30(3),e300306.
  23. Patra, S. K.(2019).How Indian libraries tweet? Word frequency and sentiment analysis of library tweets.Annals of Library and Information Studies,66(4),131-139.
  24. Pedregosa, F.,Varoquaux, G.,Gramfort, A.,Michel, V.,Thirion, B.,Grisel, O.,Duchesnay, É.(2011).Scikit-learn: Machine learning in Python.Journal of Machine Learning Research,12(85),2825-2830.
  25. Russell, J. A.(1980).A circumplex model of affect.Journal of Personality and Social Psychology,39(6),1161-1178.
  26. Sabatovych, I.(2019).Do social media create revolutions? Using Twitter sentiment analysis for predicting the Maidan Revolution in Ukraine.Global Media and Communication,15(3),275-283.
  27. Sloan, L.(Ed.),Quan-Haase, A.(Ed.)(2017).The SAGE handbook of social media research methods.Thousand Oaks, CA:SAGE.
  28. Stewart, B.,Walker, J.(2018).Build it and they will come? Patron engagement via Twitter at Historically Black College and University libraries.The Journal of Academic Librarianship,44(1),118-124.
  29. Tang, H.,Tan, S.,Cheng, X.(2009).A survey on sentiment detection of review.Expert Systems With Applications,36(7),10760-10773.
  30. Yaqub, U.,Chun, S. A.,Atluri, V.,Vaidya, J.(2021).Analyzing social media messages of public sector organizations utilizing sentiment analysis and topic modeling.Information Polity,26(4),375-390.
  31. Yu, L.-C.,Lee, L.-H.,Hao, S.,Wang, J.,He, Y.,Hu, J.,Zhang, X.(2016).Building Chinese affective resources in valence-arousal dimensions.Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,San Diego, CA:
  32. 李建華, J.,劉功申, G.,林祥, X.(2017)。情感傾向性分析及應用研究綜述。信息安全學報,2(2),48-62。
  33. 周立柱, L.-Z.,賀宇凱, Y.-K.,王建勇, J.-Y.(2008)。情感分析研究綜述。計算機應用,28(11),2725-2728。
  34. 陳迪, D.,程朗, L.,王志鋒, Z.,熊錦鵬, J.,張玉茹, Y.,梨高贊, G.(2021)。論壇情感挖掘研究綜述:現狀、挑戰與趨勢。計算機工程與應用,57(17),17-28。
  35. 陸澤凱, Z.,謝穎, Y.(2021)。微博中的「中美外交風波」輿情文本研究—基於R語言的詞向量情感分析。傳媒觀察,2021(2),54-61。
  36. 褚乃慈, N.-T.(2020)。臺北市=Taipei,世新大學資訊傳播學系=Shih Hsin University。
  37. 劉勇, I.,杜一, Y.(2017).網絡數據可視化與分析利器:Gephi中文教程.北京=Beijing:電子工業出版社=Publishing House of Electronics Industry.
  38. 闕河嘉, H.-C.,陳光華, K.-H.(2016)。庫博中文獨立語料庫分析工具之開發與應用。數位人文:在過去、現在和未來之間,臺北市=Taipei: