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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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A Photo Post Recommendation System Based on Topic Model for Improving Facebook Fan Page Engagement
(2020-07-01)
Liao, Chia-Hung
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Chen, Li-Xian
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Yang, Jhih-Cheng
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Yuan, Shyan-Ming
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資訊工程學系
Digital advertising on social media officially surpassed traditional advertising and became the largest marketing media in many countries. However, how to maximize the value of the overall marketing budget is one of the most concerning issues of all enterprises. The content of the Facebook photo post needs to be analyzed effectively so that the social media companies and managers can concentrate on handling their fan pages. This research aimed to use text mining techniques to find the audience accurately. Therefore, we built a topic model recommendation system (TMRS) to analyze Facebook posts by sorting the target posts according to the recommended scores. The TMRS includes six stages, such as data preprocessing, Chinese word segmentation, word refinement, TF-IDF word vector conversion, creating model via Latent Semantic Indexing (LSI), or Latent Dirichlet Allocation (LDA), and calculating the recommendation score. In addition to automatically selecting posts to create advertisements, this model is more effective in using marketing budgets and getting more engagements. Based on the recommendation results, it is verified that the TMRS can increase the engagement rate compared to the traditional engagement rate recommended method (ERRM). Ultimately, advertisers can have the chance to create ads for the post with potentially high engagements under a limited budget.
A Machine Learning Approach for Solar Power Technology Review and Patent Evolution Analysis
(2019-04-01)
Trappey, Amy J. C.
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Chen, Paul P. J.
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Trappey, Charles, V
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Ma, Lin
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管理科學系
Solar power systems and their related technologies have developed into a globally utilized green energy source. Given the relatively high installation costs, low conversion rates and battery capacity issues, solar energy is still not a widely applied energy source when compared to traditional energy sources. Despite the challenges, there are many innovative studies of new materials and new methods for improving solar energy transformation efficiency to improve the competitiveness of solar energy in the marketplace. This research searches for promising solar power technologies by text mining 2280 global patents and 5610 literature papers of the past decade (January 2008 to June 2018). First, a solar power knowledge ontology schema (or a key term relationship map) is constructed from the comprehensive literature and patent review. Non-supervised machine learning techniques for clustering patents and literature combined with the Latent Dirichlet Allocation (LDA) topic modeling algorithm identify sub-technology clusters and their main topics. A word-embedding algorithm is applied to identify the patent documents of the specified technologies. Cross-validation of the results is used to model the technology progress with a patent evolution map. Initial analysis show that many patents focus on solar hydropower storage systems, transferring light generated power to waterpower gravity systems. Batteries are also used but have several limitations. The objectives of this research are to review solar technology development progress and describe the innovation path that has evolved for the solar power domain. By adopting unsupervised learning approaches for literature and patent mining, this research develops a novel technology e-discovery methodology and presents the detailed reviews and analyses of the solar power technology using the proposed e-discovery workflow. The insights of global solar technology development, based on both comprehensive literature and patent reviews and cross-analyses, helps energy companies select advanced technologies related to their key technical R&D strengths and business interests. The structured solar-related technology mining can be extended to the analysis of other forms of renewable energy development.
Discovering negative comments by sentiment analysis on web forum
(2019-05-01)
Hsu, Wei-Yun
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Hsu, Hui-Huang
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Tseng, Vincent S.
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資訊工程學系
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Department of Computer Science
Social media enables people to communicate with each other on the Internet in real-time and rich styles. In other words, there is a lot of information on the social media. If we can extract negative opinions of some brands, enterprises or politics, we can use these opinions to know the market demands and solve problems. In this paper, we propose a novel approach to extract negative-sentiment-oriented features and identify negative opinions in social media with text mining and machine learning techniques, support vector machine and neural network, as well as data collection with Web crawler. The experimental results show that our proposed methods can work effectively.
Kansei Engineering with Online Review Mining for Hotel Service Development
(2017-01-01)
Hsiao, Yu-Hsiang
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Chen, Mu-Chen
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Lin, Ming-Ke
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運輸與物流管理系註：原交通所+運管所
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Department of Transportation and Logistics Management
In the Internet era, consumers can comment on their hotel living experience directly through the Internet. Thus, consumers' online comments might become a key factor for consumers choosing hotels when planning their tourism itinerary. The large amount of User-Generated Content (UGC) provided by the consumers has also provided chances for the hospitality industry to consumer behavior statistic inferences. This study applies Kansei engineering combined with text mining to develop guidelines for hotel services to help managers meet consumers' Kansei needs. Through understanding consumers' online comments by text mining, this study identifies the most important customer Kansei and hotel service characteristics, and then analyzes the relationships between them. The finds can provide suggestions for hotel service improvement and development.
Efficient Keyword-Aware Representative Travel Route Recommendation
(2017-08-01)
Wen, Yu-Ting
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Yeo, Jinyoung
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Peng, Wen-Chih
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Hwang, Seung-Won
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資訊工程學系
With the popularity of social media (e.g., Facebook and Flicker), users can easily share their check-in records and photos during their trips. In view of the huge number of user historical mobility records in social media, we aim to discover travel experiences to facilitate trip planning. When planning a trip, users always have specific preferences regarding their trips. Instead of restricting users to limited query options such as locations, activities, or time periods, we consider arbitrary text descriptions as keywords about personalized requirements. Moreover, a diverse and representative set of recommended travel routes is needed. Prior works have elaborated on mining and ranking existing routes from check-in data. To meet the need for automatic trip organization, we claim that more features of Places of Interest (POIs) should be extracted. Therefore, in this paper, we propose an efficient Keyword-aware Representative Travel Route framework that uses knowledge extraction from users' historical mobility records and social interactions. Explicitly, we have designed a keyword extraction module to classify the POI-related tags, for effective matching with query keywords. We have further designed a route reconstruction algorithm to construct route candidates that fulfill the requirements. To provide befitting query results, we explore Representative Skyline concepts, that is, the Skyline routes which best describe the trade-offs among different POI features. To evaluate the effectiveness and efficiency of the proposed algorithms, we have conducted extensive experiments on real location-based social network datasets, and the experiment results show that our methods do indeed demonstrate good performance compared to state-of-the-art works.
FIN10K: A Web-based Information System for Financial Report Analysis and Visualization
(2016)
Liu, Yu-Wen
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Liu, Liang-Chih
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Wang, Chuan-Ju
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Tsai, Ming-Feng
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資訊管理與財務金融系註：原資管所+財金所
In this demonstration, we present FIN10K, a web-based information system that facilitates the analysis of textual information in financial reports. The proposed system has three main components: (1) a 10-K Corpus, including an inverted index of financial reports on Form 10-K, several numerical finance measures, and pre-trained word embeddings; (2) an information retrieval system; and (3) two data visualizations of the analyzed results. The system can be of great help in revealing valuable insights within large amounts of textual information. The system is now online available at http: //clip.csie.org/10K/.
Automated patent document summarization for R&D intellectual property management
(2006)
Trappey, Amy J. C.
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Trappey, Charles V.
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Kao, Burgess H. S.
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交大名義發表
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National Chiao Tung University
In an era of rapid information expansion, people encounter huge amounts of intellectual property (IP) such as patents in digital format. These documents are usually too numerous to be fully utilized in R&D for new product designs. Therefore, efficient and effective ways of acquiring, organizing and presenting IPs (e.g., patent documents) have become very important for enterprises. In this paper, we propose a patent document summarization system using an integrated approach of key-phrase recognition and significant information density. Significant information density or information mass is calculated based on the summation of key-phrases, their relevant phrases, title phrases, domain-specific phrases, indicator phrases and topic sentences, divided by the total number of phrases in a paragraph or a document. External text mining game, compression ratio and retention ratio are used in system experiment and evaluation. This research enables enterprises to organize knowledge and intellectual assets efficiently and to peruse IP documents effectively.
Computer supported ontology-based patent analysis considering business processes and strategic patent portfolio management
(2015)
Trappey, Amy J. C.
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Tung, Jasmine T. C.
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Trappey, Charles
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Wang, Tong-Mei
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Tang, Mark Y. L.
Two approaches are frequently used to study the critical technology and market trends of industry. One analyzes the business processes from the market perspective with statistics market survey. The other perspective predicts new technologies from the patent (intellectual property) data. Business process analysis is limited to analyzing existing technologies and market applications. Since the service delivery processes are not frequently linked to the intellectual property of the underlying technologies, applying a patent perspective enables the analysis of technology trends, technology ownership, and royalty payment analysis. These data are used to illustrate the technology life cycles, predict emerging trends, and identify research opportunities with Computer Supported tools. Patent analysis enables the predictions of technology trends but poorly links the technology to actual products for market adoption cause of the large amount of the data. This research develops an innovative computer supported approach for technology analysis combining both market and patent perspectives. The business processes of a given knowledge domain, driven by the market perspective, is analyzed to build the basis of the domain\'s ontology. Afterward, the key phrases of the business processes are added to form the ontology schema. The related patents are then systematically linked to the business process schema nodes. The research approach enables the business process-oriented patent portfolios among current and potential competitors to be mapped and critically compared from both market performance and technology ownership perspectives. The methodology allows in-depth integrated views of rivals\' patenting and business development for strategic R&D management. The case of InvisalignTM innovative orthodontic services and the patent portfolio is studied to demonstrate the proposed methodology.
Kansei Engineering with Online Content Mining for Cross-Border Logistics Service Design
(2016)
Hsiao, Hsiang
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Chen, Mu-Chen
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運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
A satisfactory cross-border logistics service (CBLS) can help promote business activities in cross-border e-commerce. Kansei engineering (KE) is an approach to design the elements which satisfy customers\' affective and emotional perceptions into services and products. In this study, the KE approach is applied to derive ideas for the development of CBLS. For this purpose, Partial Least Squares (PLS) is used to analyze the relationships between the feelings of customers and service elements of CBLS. Moreover, this study demonstrates the applications of text mining techniques to analyze the online contents regarding CBLS. Online content mining assists in identifying the service elements and Kansei words for CBLS. Importantly, the relationship between the feelings of customers and service elements of CBLS obtained by online content mining provides complementary results for CBLS design.
Designing and Implementing a Real-Time Speech Summarizer System
(2014-01-01)
Cheng, Ding-Yuan
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Chen, Chi-Hua
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Wu, Yu-Rou
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Lo, Chi-Chun
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Lin, Hui-Fei
As the number of speech and video documents increases on the Internet and portable devices proliferate, speech summarization becomes increasingly essential. Relevant research in this domain has typically focused on broadcasts and news; however, the automatic summarization methods used in the past may not apply to other speech domains (e.g., speech in lectures). Therefore, this study explores the lecture speech domain. The features used in previous research were analyzed and suitable features were selected following experimentation; subsequently, a three-phase teal-time speech summarizer (RTSS) was proposed. Phase One involved selecting independent features (e.g. centrality, resemblance to the title, sentence length, term frequency, and thematic words) and calculating the independent feature scores; Phase Two involved calculating the dependent features, such as the position compared with the independent feature scores; and Phase Three involved comparing these feature scores to obtain weighted averages of the function-scores, determine the highest-scoring sentence, and provide a summary.

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