This page has only limited features, please log in for full access.
Shian-Hua Lin received his B.S., M.S. and a Ph.D. degree in Engineering Science from National Cheng-Kung University, Tainan, Taiwan, in 1992, 1994, and 2000, respectively. He is major in computer science. His research interests include web search engine, information extraction, machine learning, database and knowledge management system, web social network and digital learning systems. He is an associate professor of the Department of Computer Science and Information Science, National Chi Nan University now.
More and more people are involved in sustainability-related activities through social network to support/protect their idea or motivation for sustainable development. Understanding the variety of issues of social pulsation is crucial in development of social sustainability. However, issues in social media generally change overtime. Issues not identified in advance may soon become popular topics discussed in society, particularly controversial issues. Previous studies have focused on the detection of hot topics and discussion of controversial issues, rather than the identification of potential controversial issues, which truly require paying attention to social sustainability. Furthermore, previous studies have focused on issue detection and tracking based on historical data. However, not all controversial issues are related to historical data to foster the cases. To avoid the above-mentioned research gap, Artificial Intelligence (AI) plays an essential role in issue detection in the early stage. In this study, an AI-based solution approach is proposed to resolve two practical problems in social media: (1) the impact caused by the number of fan pages from Facebook and (2) awareness of the levels for an issue. The proposed solution approach to detect potential issues is based on the popularity of public opinion in social media using a Web crawler to collect daily posts related to issues in social media under a big data environment. Some analytical findings are carried out via the congregational rules proposed in this research, and the solution approach detects the attentive subjects in the early stages. A comparison of the proposed method to the traditional methods are illustrated in the domain of green energy. The computational results demonstrate that the proposed approach is accurate and effective and therefore it provides significant contribution to upsurge green energy deployment.
Chun-Che Huang; Wen-Yau Liang; Shian-Hua Lin; Tzu-Liang (Bill) Tseng; Yu-Hsien Wang; Kuo-Hsin Wu. Detection of Potential Controversial Issues for Social Sustainability: Case of Green Energy. Sustainability 2020, 12, 8057 .
AMA StyleChun-Che Huang, Wen-Yau Liang, Shian-Hua Lin, Tzu-Liang (Bill) Tseng, Yu-Hsien Wang, Kuo-Hsin Wu. Detection of Potential Controversial Issues for Social Sustainability: Case of Green Energy. Sustainability. 2020; 12 (19):8057.
Chicago/Turabian StyleChun-Che Huang; Wen-Yau Liang; Shian-Hua Lin; Tzu-Liang (Bill) Tseng; Yu-Hsien Wang; Kuo-Hsin Wu. 2020. "Detection of Potential Controversial Issues for Social Sustainability: Case of Green Energy." Sustainability 12, no. 19: 8057.
Ming-Hsun Yang; Shian-Hua Lin. Cloud-Native Approach: Educational ICT Infrastructure. International Journal of e-Education, e-Business, e-Management and e-Learning 2017, 7, 79 -84.
AMA StyleMing-Hsun Yang, Shian-Hua Lin. Cloud-Native Approach: Educational ICT Infrastructure. International Journal of e-Education, e-Business, e-Management and e-Learning. 2017; 7 (2):79-84.
Chicago/Turabian StyleMing-Hsun Yang; Shian-Hua Lin. 2017. "Cloud-Native Approach: Educational ICT Infrastructure." International Journal of e-Education, e-Business, e-Management and e-Learning 7, no. 2: 79-84.
Hao-Min Yan; Ming-Hsun Yang; Shian-Hua Lin. Lesson Fusion System (LFS) — Applying Text Analysis to Seal the Learning Gap from One-Guideline-Multiple-Text. International Journal of e-Education, e-Business, e-Management and e-Learning 2017, 7, 1 -12.
AMA StyleHao-Min Yan, Ming-Hsun Yang, Shian-Hua Lin. Lesson Fusion System (LFS) — Applying Text Analysis to Seal the Learning Gap from One-Guideline-Multiple-Text. International Journal of e-Education, e-Business, e-Management and e-Learning. 2017; 7 (1):1-12.
Chicago/Turabian StyleHao-Min Yan; Ming-Hsun Yang; Shian-Hua Lin. 2017. "Lesson Fusion System (LFS) — Applying Text Analysis to Seal the Learning Gap from One-Guideline-Multiple-Text." International Journal of e-Education, e-Business, e-Management and e-Learning 7, no. 1: 1-12.
It is very convenient to carry smartphones and take photos on the trip. With Apps installed in smartphones, users can easily share photos to friends on social networks. Huge amount of interesting photos are therefore accumulated through smartphones without facilities to manage these photos. We propose an integrated system that provides a mobile App for photographing on the trip, a desktop App for synchronizing photos with the web platform for sharing and organizing photos. While photographing, the web service recommends significant tags to photos and gets manual tags, the App transparently accumulates context-information for travel photos. Then, the desktop App facilitates users to collect and choose interesting photos for sharing and storing on the web platform. Finally, the system applies search engine and web mining techniques to extract textual sentences from pages that contain relevant information about photos for writing travel blogs easily and efficiently.
Yi-Jiu Chen; Wei-Sheng Zeng; Shian-Hua Lin. Automatic Travel Blog Generator Based on Intelligent Web Platform and Mobile Applications. Lecture Notes in Electrical Engineering 2013, 355 -364.
AMA StyleYi-Jiu Chen, Wei-Sheng Zeng, Shian-Hua Lin. Automatic Travel Blog Generator Based on Intelligent Web Platform and Mobile Applications. Lecture Notes in Electrical Engineering. 2013; ():355-364.
Chicago/Turabian StyleYi-Jiu Chen; Wei-Sheng Zeng; Shian-Hua Lin. 2013. "Automatic Travel Blog Generator Based on Intelligent Web Platform and Mobile Applications." Lecture Notes in Electrical Engineering , no. : 355-364.
Sitemaps designed by webmasters are not only presenting the main usage flows for users, but also organizing the hierarchical concept of the website. However, websites seldom provide sitemap pages to facilitate users to browse pages easily. Even provided, these sitemaps are not for machine-understanding, although few websites provide sitemaps with the XML format. In this paper, we develop a system, SiteMap Generator (SMG), to automatically generate the hierarchical sitemap for a website. SMG consists of five components. Sequence Translator translates a page’s HTML source into a long sequence and then Page Partitioner splits the page into blocks based on analyzing the sequence complexity. Block Identifier categorizes each block into one of three block types: content, structure or redundant. Using the popular sequence searching tool, BLAST, Block Cluster calculates similarities between blocks so that blocks with similar functionalities are grouped and considered as candidate blocks for the sitemap. Finally, Hyperlink Analyzer transforms page-to-page links into block-to-block links and applies Kleinberg’s HITS algorithm to estimate authority and hub values of each block. Block entropy value derived from features entropies is also used to improve the HITS. Several experiments on three websites: Mozilla, CNN and Yahoo! News, show that SMG is useful to partition a page into blocks (F1 = 86%), identify the block type (F1 = 85%), and generate the sitemap for the website (F1 = 63%).
Shian-Hua Lin; Kuan-Pak Chu; Chun-Ming Chiu. Automatic sitemaps generation: Exploring website structures using block extraction and hyperlink analysis. Expert Systems with Applications 2011, 38, 3944 -3958.
AMA StyleShian-Hua Lin, Kuan-Pak Chu, Chun-Ming Chiu. Automatic sitemaps generation: Exploring website structures using block extraction and hyperlink analysis. Expert Systems with Applications. 2011; 38 (4):3944-3958.
Chicago/Turabian StyleShian-Hua Lin; Kuan-Pak Chu; Chun-Ming Chiu. 2011. "Automatic sitemaps generation: Exploring website structures using block extraction and hyperlink analysis." Expert Systems with Applications 38, no. 4: 3944-3958.
Automatic emotion sensing in textual data is crucial for the development of intelligent interfaces in many interactive computer applications. This paper describes a high-precision, knowledgebase-independent approach for automatic emotion sensing for the subjects of events embedded within sentences. The proposed approach is based on the probability distribution of common mutual actions between the subject and the object of an event. We have incorporated web-based text mining and semantic role labeling techniques, together with a number of reference entity pairs and hand-crafted emotion generation rules to realize an event emotion detection system. The evaluation outcome reveals a satisfactory result with about 85% accuracy for detecting the positive, negative and neutral emotions.
Cheng-Yu Lu; Shian-Hua Lin; Jen-Chang Liu; Samuel Cruz-Lara; Jen-Shin Hong. Automatic event-level textual emotion sensing using mutual action histogram between entities. Expert Systems with Applications 2010, 37, 1643 -1653.
AMA StyleCheng-Yu Lu, Shian-Hua Lin, Jen-Chang Liu, Samuel Cruz-Lara, Jen-Shin Hong. Automatic event-level textual emotion sensing using mutual action histogram between entities. Expert Systems with Applications. 2010; 37 (2):1643-1653.
Chicago/Turabian StyleCheng-Yu Lu; Shian-Hua Lin; Jen-Chang Liu; Samuel Cruz-Lara; Jen-Shin Hong. 2010. "Automatic event-level textual emotion sensing using mutual action histogram between entities." Expert Systems with Applications 37, no. 2: 1643-1653.