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Prof. Long-Sheng Chen
Chaoyang University of Technology

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Research Keywords & Expertise

0 Data Mining
0 Feature Selection
0 Social Media Marketing
0 Text Mining
0 Social Media Data Mining

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Feature Selection
Text Mining
Data Mining
Social Media Marketing

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Short Biography

Long-Sheng Chen is a Professor in the Department of Information Management, Chaoyang University of Technology (CYUT), Taiwan. From 2018 to now, he is Assistant Vice President for Academic Affairs of CYUT. He received his Ph.D. in the Department of Industrial Engineering and Management, National Chiao Tung University, Taiwan, in 2006, and his BS and MS degrees, both in industrial management, from National Cheng Kung University, Tainan, Taiwan, in 1998 and 2000, respectively. His research activities include data mining, text mining, social media marketing, quality management, and customer relationship management.

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Journal article
Published: 14 July 2021 in Applied Soft Computing
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Live streaming has become one of the leisure activities of most people due to the rich and various contents. For young generation, to watch other people playing games on the live streaming platform is becoming very popular. Related researches mainly focused on predicting the number of viewers, finding popular streamer, studying the gift giving behaviors, and so on. Relatively few studies focused on how viewers’ comments affect users’ viewing behaviors, since the power of text comments in social media have been confirmed. In addition, published studies usually employed questionnaire survey methods which are prone to experimental effects. And online text comments will be more objective and less sampling bias than data collected by questionnaires. Consequently, this study focuses live streaming of games and uses viewers’ text comments for experimental analysis. A text mining-based framework which includes Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Chi-square test will be proposed to determine the important keywords of predicting the number of views in live streaming. Support Vector Machine (SVM) will be utilized to evaluate the performances of candidate feature subsets. Then, K-means and Latent Semantic Analysis (LSA) using Singular Value Decomposition (SVD) have been used to organize the selected keywords into understandable concepts. Real cases of game live streaming cases will be collected from Twitch.tv for our experiments. Results can be used as a reference for live streaming platforms and live channels, and help them to increase the number of viewers for further income enhancement.

ACS Style

Wen-Kuo Chen; Long-Sheng Chen; Yi-Ting Pan. A text mining-based framework to discover the important factors in text reviews for predicting the views of live streaming. Applied Soft Computing 2021, 111, 107704 .

AMA Style

Wen-Kuo Chen, Long-Sheng Chen, Yi-Ting Pan. A text mining-based framework to discover the important factors in text reviews for predicting the views of live streaming. Applied Soft Computing. 2021; 111 ():107704.

Chicago/Turabian Style

Wen-Kuo Chen; Long-Sheng Chen; Yi-Ting Pan. 2021. "A text mining-based framework to discover the important factors in text reviews for predicting the views of live streaming." Applied Soft Computing 111, no. : 107704.

Original research
Published: 23 March 2021 in Journal of Ambient Intelligence and Humanized Computing
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The emergence of crowdfunding has given many capital demanders a new fund-raising channel, but the overall project success rate is very low. Many scholars have begun to discover key suscessful factors of crowdfunding projects. Previous studies have used questionnaires survey to identify important project features. In addition to requiring a lot of manpower and time, there may also be sampling bias. Moreover, related studies also reported that the project description will affect the success of the crowdfunding project, but there is no research to tell fundraisers which success factors should be included in the content of the project description. Besides, in recent years, game crowdfunding projects have been attracted lots of attention in terms of total fundraising amount and number of projects. Moreover, in traditional feature selection and text mining approaches, the selected terms are un-organized and hard to be explained. Therefore, this study will focus on real video and mobile game project descriptions to replace conventional questionnaires. To solve these issues, we present a lexicon-based feature selection method. We attempt to define “content features” and build lexicons to determine the attributes’ values. Three feature selection methods including decision tree (DT), Least Absolute Shrinkage and Selection Operator (LASSO), and support vector machine–recursive feature elimination (SVM–RFE) will be employed to find organized candidate key successful factors. Then, support vector machines (SVM) will be used to evaluate the performances of candidate feature subsets. Finally, this study has identified the key successful factors for video and mobile games, respectively. Based on the experimental results, we can give fundraisers some useful suggestions to improve the success rate of crowdfunding projects.

ACS Style

Mu-Yen Chen; Jing-Rong Chang; Long-Sheng Chen; En-Li Shen. The key successful factors of video and mobile game crowdfunding projects using a lexicon-based feature selection approach. Journal of Ambient Intelligence and Humanized Computing 2021, 1 -19.

AMA Style

Mu-Yen Chen, Jing-Rong Chang, Long-Sheng Chen, En-Li Shen. The key successful factors of video and mobile game crowdfunding projects using a lexicon-based feature selection approach. Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-19.

Chicago/Turabian Style

Mu-Yen Chen; Jing-Rong Chang; Long-Sheng Chen; En-Li Shen. 2021. "The key successful factors of video and mobile game crowdfunding projects using a lexicon-based feature selection approach." Journal of Ambient Intelligence and Humanized Computing , no. : 1-19.

Journal article
Published: 30 December 2020 in Sustainability
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Due to the COVID-19 pandemic, the sales of fast-food businesses have dropped sharply. Customer satisfaction has always been one of the key factors for the sustainable development of enterprises. However, in the fast-food restaurant business, gaining the knowledge of customer satisfaction is one of the critical tasks. Moreover, text reviews in social media have become one of important reference sources for customers’ decisions in buying services and products. Therefore, the main purpose of this study is to explore whether customer voices from social media reviews are different during the COVID-19 outbreak and to propose a new method to reduce interpersonal contact when collecting data. A text mining scheme which includes least absolute shrinkage and selection operator (LASSO) and decision trees (DT) are presented to discover the essential factors for customers to increase their satisfaction from unstructured online customer reviews. Finally, three real world review sets were employed to validate the effectiveness of the presented text mining scheme. Experimental results can help companies to properly adapt to similar epidemic situations in the future and facilitate their sustainable development.

ACS Style

Wen-Kuo Chen; Dalianus Riantama; Long-Sheng Chen. Using a Text Mining Approach to Hear Voices of Customers from Social Media toward the Fast-Food Restaurant Industry. Sustainability 2020, 13, 268 .

AMA Style

Wen-Kuo Chen, Dalianus Riantama, Long-Sheng Chen. Using a Text Mining Approach to Hear Voices of Customers from Social Media toward the Fast-Food Restaurant Industry. Sustainability. 2020; 13 (1):268.

Chicago/Turabian Style

Wen-Kuo Chen; Dalianus Riantama; Long-Sheng Chen. 2020. "Using a Text Mining Approach to Hear Voices of Customers from Social Media toward the Fast-Food Restaurant Industry." Sustainability 13, no. 1: 268.

Journal article
Published: 30 September 2020 in Sensors
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The Internet of Things (IoT) is currently the most popular field in communication and information techniques. However, designing a secure and reliable authentication scheme for IoT-based architectures is still a challenge. In 2019, Zhou et al. showed that schemes pro-posed by Amin et al. and Maitra et al. are vulnerable to off-line guessing attacks, user tracking attacks, etc. On this basis, a lightweight authentication scheme based on IoT is proposed, and an authentication scheme based on IoT is proposed, which can resist various types of attacks and realize key security features such as user audit, mutual authentication, and session security. However, we found weaknesses in the scheme upon evaluation. Hence, we proposed an enhanced scheme based on their mechanism, thus achieving the security requirements and resisting well-known attacks.

ACS Style

Hsiao-Ling Wu; Chin-Chen Chang; Yao-Zhu Zheng; Long-Sheng Chen; Chih-Cheng Chen. A Secure IoT-Based Authentication System in Cloud Computing Environment. Sensors 2020, 20, 5604 .

AMA Style

Hsiao-Ling Wu, Chin-Chen Chang, Yao-Zhu Zheng, Long-Sheng Chen, Chih-Cheng Chen. A Secure IoT-Based Authentication System in Cloud Computing Environment. Sensors. 2020; 20 (19):5604.

Chicago/Turabian Style

Hsiao-Ling Wu; Chin-Chen Chang; Yao-Zhu Zheng; Long-Sheng Chen; Chih-Cheng Chen. 2020. "A Secure IoT-Based Authentication System in Cloud Computing Environment." Sensors 20, no. 19: 5604.

Original research
Published: 14 August 2020 in Journal of Ambient Intelligence and Humanized Computing
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Text based social media has become one of important communication tools between customers and enterprises. In social media, users can easily express their opinions and evaluation regarding products or services. These online user experiences, especially negative evaluations indeed affect other consumers’ behaviors. Consequently, to effectively identify customers’ sentiments and avoid these negative comments to bring a great damage to enterprisers has become one of critical issues. In recent years, machine learning algorithms were viewed as one of effective solutions for sentiment classification. But, when the amount of the online reviews arises, the dimensionality of text data rises remarkably. The performances of machine learning methods have been degraded due to the dimensionality problem. But, conventional feature selection methods tend to select attributes from the majority sentiments, which usually cannot improve classification performance. Therefore, this study attempt to present two feature selection methods called modified categorical proportional difference (MCPD) approach that improves conventional CPD method, and balance category feature (BCF) strategy that equally selects attributes from both positive and negative examples, to improve sentiment classification performances. Finally, several real sentiment cases of text reviews will be provided to demonstrate the effectiveness of our proposed methods. Results showed that the combination of proposed BCF strategy and MCPD method can not only remarkably reduce feature space, but also improve the sentiment classification performance.

ACS Style

Jing-Rong Chang; Hsin-Ying Liang; Long-Sheng Chen; Chia-Wei Chang. Novel feature selection approaches for improving the performance of sentiment classification. Journal of Ambient Intelligence and Humanized Computing 2020, 1 -14.

AMA Style

Jing-Rong Chang, Hsin-Ying Liang, Long-Sheng Chen, Chia-Wei Chang. Novel feature selection approaches for improving the performance of sentiment classification. Journal of Ambient Intelligence and Humanized Computing. 2020; ():1-14.

Chicago/Turabian Style

Jing-Rong Chang; Hsin-Ying Liang; Long-Sheng Chen; Chia-Wei Chang. 2020. "Novel feature selection approaches for improving the performance of sentiment classification." Journal of Ambient Intelligence and Humanized Computing , no. : 1-14.

Conference paper
Published: 01 July 2020 in Proceedings of the 2020 International Conference on Management of e-Commerce and e-Government
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Medical fraudulent activities have made medical insurance expenditures rise year by year. This not only increases the burden on the medical and financial system, but also makes it difficult for many people in need to obtain these resources. Therefore, how to solve this problem has become one of critical issues. Therefore, this study aims to establish a predictive model of medical insurance fraud through data mining methods, and attempts to discover important factors affecting fraud. In this work, we will use Decision Tree (DT), Support Vector Machines (SVM), and Back Propagation Neural Networks (BPN) to establish classification models. A comparison of these three methods will be done. And, we will use decision trees to extract important factors that could provide important information for effectively detect medical fraud. Hopefully, we can effectively reduce the negative impact of medical insurance fraud.

ACS Style

Long-Sheng Chen; Jia-Chuan Chen. Using Data Mining Methods to Detect Medical Fraud. Proceedings of the 2020 International Conference on Management of e-Commerce and e-Government 2020, 1 .

AMA Style

Long-Sheng Chen, Jia-Chuan Chen. Using Data Mining Methods to Detect Medical Fraud. Proceedings of the 2020 International Conference on Management of e-Commerce and e-Government. 2020; ():1.

Chicago/Turabian Style

Long-Sheng Chen; Jia-Chuan Chen. 2020. "Using Data Mining Methods to Detect Medical Fraud." Proceedings of the 2020 International Conference on Management of e-Commerce and e-Government , no. : 1.

Journal article
Published: 18 June 2020 in Pervasive and Mobile Computing
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The Internet of Things technology allows devices automatically connect with others or a server for the purposes of exchanging data. People can conveniently integrate data from those devices for a smart home, vehicular ad-hoc network, e-Health, etc. In 2017, Wang et al. proposed a simple authentication scheme for the Internet of Things. Although they formally proved that their scheme is secure, they did not consider the privacy of devices and stolen verifier attack. In this paper, we first demonstrate the weaknesses of Wang et al.’s scheme. Accordingly, we present a higher security level authentication scheme to resist the above weaknesses.

ACS Style

Hsiao-Ling Wu; Chin-Chen Chang; Long-Sheng Chen. Secure and anonymous authentication scheme for the Internet of Things with pairing. Pervasive and Mobile Computing 2020, 67, 101177 .

AMA Style

Hsiao-Ling Wu, Chin-Chen Chang, Long-Sheng Chen. Secure and anonymous authentication scheme for the Internet of Things with pairing. Pervasive and Mobile Computing. 2020; 67 ():101177.

Chicago/Turabian Style

Hsiao-Ling Wu; Chin-Chen Chang; Long-Sheng Chen. 2020. "Secure and anonymous authentication scheme for the Internet of Things with pairing." Pervasive and Mobile Computing 67, no. : 101177.

Research article
Published: 16 October 2019 in Security and Communication Networks
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Blockchain is an emerging technology that promises many exciting applications in various fields, including financial, medical, energy, and logistics management. However, there are still some limitations in the existing blockchain framework that prevents its widespread adoption in the commercial world. One important limitation is the storage requirement, wherein each blockchain node has to store a copy of the distributed ledger. Thus, as the number of transactions increases, this storage requirement grows quadratically, eventually limiting the scalability of a blockchain system. Moreover, the public ledger in a blockchain framework allows anyone in the network to audit the transaction, which may not be favourable in some privacy-sensitive applications. In this paper, a secret-sharing scheme is proposed to reduce the size of the blockchain transactions. Each transaction block is divided into t parts, and the size of each part is 1/t size of transaction block. We use the secret-sharing mechanism to share t parts into n shares. Hence, each node stores not one transaction but one share in the blockchain system. The proposed scheme can eventually reduce the storage cost of a blockchain transaction by 1/t without introducing an additional recovery communication cost; however, robustness is reduced in node failure as a tradeoff. Meanwhile, the proposed scheme was more efficient and secure compared to other state-of-the-art schemes that aim to reduce blockchain storage for industrial big data.

ACS Style

Hefeng Chen; Hsiao-Ling Wu; Chin-Chen Chang; Long-Sheng Chen. Light Repository Blockchain System with Multisecret Sharing for Industrial Big Data. Security and Communication Networks 2019, 2019, 1 -7.

AMA Style

Hefeng Chen, Hsiao-Ling Wu, Chin-Chen Chang, Long-Sheng Chen. Light Repository Blockchain System with Multisecret Sharing for Industrial Big Data. Security and Communication Networks. 2019; 2019 ():1-7.

Chicago/Turabian Style

Hefeng Chen; Hsiao-Ling Wu; Chin-Chen Chang; Long-Sheng Chen. 2019. "Light Repository Blockchain System with Multisecret Sharing for Industrial Big Data." Security and Communication Networks 2019, no. : 1-7.

Journal article
Published: 08 October 2019 in IEEE Access
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The development of social media has changed the way that travelers visit sightseeing spots. The social Internet of Things (IoT) allows products to automatically generate posts, share content and location information, and help build an online community of users based on their company’s products, so that marketing personnel can also get useful feedback and understand the user’s opinions. In tourism and hospitality industry, to enhance the revisit intention of passengers is an important issue for the purpose of increasing margin. In recent years, related researches had focused on the customers’ revisit behaviors and factors. However, few studies have investigated the related issues that travelers do not want to visit again. Failure to revisit may bring a great damage to the company’s revenue in the future. To avoid this situation, a text mining based approach will be proposed to identify non-revisit factors from online textual reviews in social media. Because it is impossible to determine whether a passenger has intention to revisit, this study proposed a text mining based approach which uses sentiment of text reviews to identify the passenger’s motivations (negative for revisit and non-negative for revisit). Then, feature selection methods, decision tree (DT), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machines Recursive Feature Elimination (SVM-RFE) will be utilized to discover the important factors of non-revisit factor set. Back-propagation Neural Networks (BPN) and Support Vector Machines (SVM) will be employed to evaluate the effectiveness of selected feature sets. Finally, experimental results could be provided to travel service providers to improve service quality and effectively avoid non-revisit behaviors in the future.

ACS Style

Jing-Rong Chang; Mu-Yen Chen; Long-Sheng Chen; Shu-Cih Tseng. Why Customers Don’t Revisit in Tourism and Hospitality Industry? IEEE Access 2019, 7, 146588 -146606.

AMA Style

Jing-Rong Chang, Mu-Yen Chen, Long-Sheng Chen, Shu-Cih Tseng. Why Customers Don’t Revisit in Tourism and Hospitality Industry? IEEE Access. 2019; 7 (99):146588-146606.

Chicago/Turabian Style

Jing-Rong Chang; Mu-Yen Chen; Long-Sheng Chen; Shu-Cih Tseng. 2019. "Why Customers Don’t Revisit in Tourism and Hospitality Industry?" IEEE Access 7, no. 99: 146588-146606.

Review
Published: 01 October 2019 in 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)
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ACS Style

Long-Sheng Chen; Ying-Jung Chuang. A Study of Social Media Reviews Effects on the Success of Crowdfunding Projects. 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) 2019, 1 .

AMA Style

Long-Sheng Chen, Ying-Jung Chuang. A Study of Social Media Reviews Effects on the Success of Crowdfunding Projects. 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST). 2019; ():1.

Chicago/Turabian Style

Long-Sheng Chen; Ying-Jung Chuang. 2019. "A Study of Social Media Reviews Effects on the Success of Crowdfunding Projects." 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) , no. : 1.

Conference paper
Published: 01 October 2019 in 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST)
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ACS Style

Shu-Cih Tseng; Yu-Ching Lu; Goutam Chakraborty; Long-Sheng Chen. Comparison of Sentiment Analysis of Review Comments by Unsupervised Clustering of Features Using LSA and LDA. 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) 2019, 1 .

AMA Style

Shu-Cih Tseng, Yu-Ching Lu, Goutam Chakraborty, Long-Sheng Chen. Comparison of Sentiment Analysis of Review Comments by Unsupervised Clustering of Features Using LSA and LDA. 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST). 2019; ():1.

Chicago/Turabian Style

Shu-Cih Tseng; Yu-Ching Lu; Goutam Chakraborty; Long-Sheng Chen. 2019. "Comparison of Sentiment Analysis of Review Comments by Unsupervised Clustering of Features Using LSA and LDA." 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) , no. : 1.

Conference paper
Published: 07 July 2019 in Proceedings of the 2019 International Electronics Communication Conference
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ACS Style

Hsiao-Ling Wu; Chin-Chen Chang; Long-Sheng Chen. Secure Authentication Scheme with Conditional Privacy Preservation in a Global Mobility Communication Network. Proceedings of the 2019 International Electronics Communication Conference 2019, 1 .

AMA Style

Hsiao-Ling Wu, Chin-Chen Chang, Long-Sheng Chen. Secure Authentication Scheme with Conditional Privacy Preservation in a Global Mobility Communication Network. Proceedings of the 2019 International Electronics Communication Conference. 2019; ():1.

Chicago/Turabian Style

Hsiao-Ling Wu; Chin-Chen Chang; Long-Sheng Chen. 2019. "Secure Authentication Scheme with Conditional Privacy Preservation in a Global Mobility Communication Network." Proceedings of the 2019 International Electronics Communication Conference , no. : 1.

Proceedings article
Published: 01 July 2019 in 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI)
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ACS Style

Long-Sheng Chen; Mu-Chen Chen; Yi-Ru Lin. Customer Needs Analysis for Overseas Purchasing in Taiwan. 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI) 2019, 1 .

AMA Style

Long-Sheng Chen, Mu-Chen Chen, Yi-Ru Lin. Customer Needs Analysis for Overseas Purchasing in Taiwan. 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI). 2019; ():1.

Chicago/Turabian Style

Long-Sheng Chen; Mu-Chen Chen; Yi-Ru Lin. 2019. "Customer Needs Analysis for Overseas Purchasing in Taiwan." 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI) , no. : 1.

Focus
Published: 30 April 2019 in Soft Computing
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Online-to-offline/offline-to-online (O2O) business models have attracted lots of enterprisers to enter this market. In such a fast-growing competition, some studies indicated that lack of trust will bring a great damage to O2O business. Related works already confirm trust is the key factor to the success of O2O. Besides, social media has been changing the way providers communicate with consumers. Negative comments in social media will decrease the consumers’ trust to O2O companies and platforms. Available O2O studies are almost always conducted by means of questionnaires or interviews, which cannot provide immediate customer response and require a lot of manpower and time. Since online reviews are the main information source for consumers. Therefore, this study presented a text mining-based scheme which uses text mining technique to find important factors from online electronic word-of-mouth, to replace the traditional questionnaire survey method of collecting data. Two feature selection methods, Support Vector Machines Recursive Feature Elimination and Least Absolute Shrinkage and Selection Operator have employed to select important factors that affect O2O trust. We also evaluate the performance of extracted feature subsets by Support Vector Machines. The findings can be referenced for O2O market enterprises to carefully response customers’ comments to avoid hurting customers’ trust and improve service quality.

ACS Style

Jing-Rong Chang; Mu-Yen Chen; Long-Sheng Chen; Wan-Ting Chien. Recognizing important factors of influencing trust in O2O models: an example of OpenTable. Soft Computing 2019, 24, 7907 -7923.

AMA Style

Jing-Rong Chang, Mu-Yen Chen, Long-Sheng Chen, Wan-Ting Chien. Recognizing important factors of influencing trust in O2O models: an example of OpenTable. Soft Computing. 2019; 24 (11):7907-7923.

Chicago/Turabian Style

Jing-Rong Chang; Mu-Yen Chen; Long-Sheng Chen; Wan-Ting Chien. 2019. "Recognizing important factors of influencing trust in O2O models: an example of OpenTable." Soft Computing 24, no. 11: 7907-7923.

Conference paper
Published: 01 April 2019 in 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA)
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Due to the advancement of learning devices, it helps to educate the younger generation on Internet. No matter where you are, as long as smart devices can connect to the Internet, learning is endless. In mobile learning, multimedia is an important medium. So, how to design multimedia for satisfying the needs of users is one of crucial issues. Therefore, this study mainly uses the theory of attractive quality (Kano model) to analyze the needs of users for multimedia design in e-learning. It can offer a better understanding of how customers evaluate a product, and assists companies with focusing on the most important attributes that need to be improved. This study aims to find the user's expectations and needs for e-learning materials. From the perspective of multimedia designers, the quality elements could be defined. Through Kano analysis and extracted rules of decision trees, we could find the categorization of quality elements, which are relevant to e-learning material designers for producing more user-friendly multimedia files.

ACS Style

Long-Sheng Chen; Jui-Yang Hsu. Discover Users’ Needs in e-Learning by Kano Analysis and Decision Trees. 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA) 2019, 572 -576.

AMA Style

Long-Sheng Chen, Jui-Yang Hsu. Discover Users’ Needs in e-Learning by Kano Analysis and Decision Trees. 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA). 2019; ():572-576.

Chicago/Turabian Style

Long-Sheng Chen; Jui-Yang Hsu. 2019. "Discover Users’ Needs in e-Learning by Kano Analysis and Decision Trees." 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA) , no. : 572-576.

Conference paper
Published: 01 April 2019 in 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA)
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Because the rise of crowdfunding, entrepreneurs decrease seeking help from traditionally financial institutions, but began to get help on the Internet. Now, more than 15 million people involved and the amount of funds raised exceeded 3.9 billion US dollars. Although crowdfunding provides a new fundraising channel for entrepreneurs who need to raise funds, success in reaching the target amount is a big challenge. How to increase the success rate of fundraising projects is the most concern of all fundraisers. Most of the current researches aimed to explore the relation between the founders and the success of the project. Relatively few works focus on the impact of the description and the wording of the project to predict the success rate of fundraising. Therefore, this study will collect real fundraising projects from Kickstarter, and analyze the text description content of these projects. The feature selection method, Support Vector Machines Recursive Feature Elimination (SVM-RFE), has been employed to find key words that may affect the success of the project. Then, we'll use selected keywords to build a prediction model by utilizing Support Vector Machines (SVM) to help emerging entrepreneurs or anyone who needs to raise funds can have a higher chance of successful fundraising.

ACS Style

Long-Sheng Chen; En-Li Shen. Finding the Keywords Affecting the Success of Crowdfunding Projects. 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA) 2019, 567 -571.

AMA Style

Long-Sheng Chen, En-Li Shen. Finding the Keywords Affecting the Success of Crowdfunding Projects. 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA). 2019; ():567-571.

Chicago/Turabian Style

Long-Sheng Chen; En-Li Shen. 2019. "Finding the Keywords Affecting the Success of Crowdfunding Projects." 2019 IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA) , no. : 567-571.

Conference paper
Published: 11 November 2018 in Blockchain Technology and Innovations in Business Processes
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Watching other people playing games on live streaming platforms have become more popular. In published literature, most of researches on live streaming focused on predicting the number of viewers in the live streaming period, explaining the high peak of the audience in a game, and finding out popular live streamers, and discussing usage behaviors such as exploring the gift giving. However, from available literature, relatively few works focus on discussing the text chats/comments which can affect other users’ watching behaviors. Therefore, this study aims to find important terms that affect viewing of live streaming. We used live game streaming as our study target. Using the comments of the audience in the chat room of the Twitch live streaming platform as experimental samples. Text mining and feature selection methods, including Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature (SVM-RFE) and chi-square test (χ^2 test), to find important terms that affect viewing of live streaming.

ACS Style

Long-Sheng Chen; Yi-Ting Pan. The Keywords of Affecting Performance for Live Streaming. Blockchain Technology and Innovations in Business Processes 2018, 241 -248.

AMA Style

Long-Sheng Chen, Yi-Ting Pan. The Keywords of Affecting Performance for Live Streaming. Blockchain Technology and Innovations in Business Processes. 2018; ():241-248.

Chicago/Turabian Style

Long-Sheng Chen; Yi-Ting Pan. 2018. "The Keywords of Affecting Performance for Live Streaming." Blockchain Technology and Innovations in Business Processes , no. : 241-248.

Conference paper
Published: 01 September 2018 in 2018 9th International Conference on Awareness Science and Technology (iCAST)
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The population of tourists has grown rapidly with the development of social media. The rise of social media has changed the behaviors that passengers visit sightseeing spots. Online consumer reviews could be considered as the main channel for providing valuable information to consumers. Revisit intention could directly influence the future behavior of customers. It's also one of the crucial factors that enhance the income growth of tourism. However, relatively few researchers focused on why passengers don't revisit directly. Therefore, this study will focus on the topic that why passengers didn't revisit again. We'll use textual reviews of social media instead of questionnaire survey. And text mining and feature selection (Least Absolute Shrinkage and Selection Operator, LASSO) methods have been employed to identify the factors that affect passenger non-revisit intention. From the results, this study will provide some suggestions for the tourism industry to improve their service quality and increase their revisit intentions.

ACS Style

Long-Sheng Chen; Shu-Cih Tseng; Goutam Chakraborty. Why Tourists Don’t Visit Again? 2018 9th International Conference on Awareness Science and Technology (iCAST) 2018, 236 -240.

AMA Style

Long-Sheng Chen, Shu-Cih Tseng, Goutam Chakraborty. Why Tourists Don’t Visit Again? 2018 9th International Conference on Awareness Science and Technology (iCAST). 2018; ():236-240.

Chicago/Turabian Style

Long-Sheng Chen; Shu-Cih Tseng; Goutam Chakraborty. 2018. "Why Tourists Don’t Visit Again?" 2018 9th International Conference on Awareness Science and Technology (iCAST) , no. : 236-240.

Conference paper
Published: 01 September 2018 in 2018 9th International Conference on Awareness Science and Technology (iCAST)
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Recently, O2O (Online to Offline/Offline to Online) business model is growing rapidly. While selecting a service from various options, many researchers pointed out that customers` trust is an important issue. To grow business, it is necessary to enhance the customer's trust on O2O platforms. In previous studies, questionnaire survey data was used to find crucial factors. But gathering survey from customers need manpower and time. Moreover, customers' comments are restricted by the set of questions, and could not describe their thoughts explicitly. In this work. We use textual comments by customer as source data to find important factors.The textual review data obtained over internet contains more positive review than negative. This imbalance in data, when used for classifier, gives high classification score for positive reviews but fail to identify negative reviews. We found, through experiments, that balancing data before feature selection as well as while training the classifier gives highest score for overall classification, the geometric mean of positive and negative accuracies. We used LASSO for feature selection and undersampling to create balanced data. Results before and after balancing data are shown. The selected features specigy the understanding of service provider, where they should emphasize for increased trust and therefore enhance their business.

ACS Style

Wan-Ting Chien; Goutam Chakraborty; Long-Sheng Chen. Identifying important factors affecting O2O customers trust from textual reviews. 2018 9th International Conference on Awareness Science and Technology (iCAST) 2018, 117 -121.

AMA Style

Wan-Ting Chien, Goutam Chakraborty, Long-Sheng Chen. Identifying important factors affecting O2O customers trust from textual reviews. 2018 9th International Conference on Awareness Science and Technology (iCAST). 2018; ():117-121.

Chicago/Turabian Style

Wan-Ting Chien; Goutam Chakraborty; Long-Sheng Chen. 2018. "Identifying important factors affecting O2O customers trust from textual reviews." 2018 9th International Conference on Awareness Science and Technology (iCAST) , no. : 117-121.

Conference paper
Published: 01 July 2018 in 2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)
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The development of social media has changed the way that travelers visit sightseeing spots. In tourism and hospitality industry, to enhance the revisit intention of passengers is an important issue for the purpose of increasing margin. In recent years, related researches had focused on the customers' revisit behaviors and factors. But, few studies have investigated the related issues that travelers do not want to visit again. Failure to revisit may bring a great damage to the company's revenue in the future. To avoid the occurrence of these injuries, a text mining approach will be employed to discover the reason why customers don't revisit from online textual reviews in social media. In this work, we attempt to define the candidate factors that may influence the non-revisit, and then use two feature selection methods, decision tree and Support Vector Machines -Recursive Feature Elimination (SVM-RFE) to find the crucial factors. Experimental results could be provided to travel service providers to improve service quality and effectively avoid future impact on passengers no longer visiting.

ACS Style

Jing-Rong Chang Chang; Long-Sheng Chen; Shu-Cih Tseng. Apply Data Mining Approach to Identify Non-revisit Factors for Hotel Industry. 2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI) 2018, 671 -675.

AMA Style

Jing-Rong Chang Chang, Long-Sheng Chen, Shu-Cih Tseng. Apply Data Mining Approach to Identify Non-revisit Factors for Hotel Industry. 2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI). 2018; ():671-675.

Chicago/Turabian Style

Jing-Rong Chang Chang; Long-Sheng Chen; Shu-Cih Tseng. 2018. "Apply Data Mining Approach to Identify Non-revisit Factors for Hotel Industry." 2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI) , no. : 671-675.