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network analysis</title> <meta name="description" content="Search results for: keyword network analysis"> <meta name="keywords" content="keyword network analysis"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="keyword network analysis"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 31070</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: keyword network analysis</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31070</span> Keyword Network Analysis on the Research Trends of Life-Long Education for People with Disabilities in Korea</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jakyoung%20Kim">Jakyoung Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Sungwook%20Jang"> Sungwook Jang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study is to examine the research trends of life-long education for people with disabilities using a keyword network analysis. For this purpose, 151 papers were selected from 594 papers retrieved using keywords such as 'people with disabilities' and 'life-long education' in the Korean Education and Research Information Service. The Keyword network analysis was constructed by extracting and coding the keyword used in the title of the selected papers. The frequency of the extracted keywords, the centrality of degree, and betweenness was analyzed by the keyword network. The results of the keyword network analysis are as follows. First, the main keywords that appeared frequently in the study of life-long education for people with disabilities were 'people with disabilities', 'life-long education', 'developmental disabilities', 'current situations', 'development'. The research trends of life-long education for people with disabilities are focused on the current status of the life-long education and the program development. Second, the keyword network analysis and visualization showed that the keywords with high frequency of occurrences also generally have high degree centrality and betweenness centrality. In terms of the keyword network diagram, it was confirmed that research trends of life-long education for people with disabilities are centered on six prominent keywords. Based on these results, it was discussed that life-long education for people with disabilities in the future needs to expand the subjects and the supporting areas of the life-long education, and the research needs to be further expanded into more detailed and specific areas.  <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=life-long%20education" title="life-long education">life-long education</a>, <a href="https://publications.waset.org/abstracts/search?q=people%20with%20disabilities" title=" people with disabilities"> people with disabilities</a>, <a href="https://publications.waset.org/abstracts/search?q=research%20trends" title=" research trends"> research trends</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword%20network%20analysis" title=" keyword network analysis"> keyword network analysis</a> </p> <a href="https://publications.waset.org/abstracts/72646/keyword-network-analysis-on-the-research-trends-of-life-long-education-for-people-with-disabilities-in-korea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72646.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">338</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31069</span> Analyzing Keyword Networks for the Identification of Correlated Research Topics </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thiago%20M.%20R.%20Dias">Thiago M. R. Dias</a>, <a href="https://publications.waset.org/abstracts/search?q=Patr%C3%ADcia%20M.%20Dias"> Patrícia M. Dias</a>, <a href="https://publications.waset.org/abstracts/search?q=Gray%20F.%20Moita"> Gray F. Moita</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The production and publication of scientific works have increased significantly in the last years, being the Internet the main factor of access and distribution of these works. Faced with this, there is a growing interest in understanding how scientific research has evolved, in order to explore this knowledge to encourage research groups to become more productive. Therefore, the objective of this work is to explore repositories containing data from scientific publications and to characterize keyword networks of these publications, in order to identify the most relevant keywords, and to highlight those that have the greatest impact on the network. To do this, each article in the study repository has its keywords extracted and in this way the network is  characterized, after which several metrics for social network analysis are applied for the identification of the highlighted keywords. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bibliometrics" title="bibliometrics">bibliometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=extraction%20and%20data%20integration" title=" extraction and data integration"> extraction and data integration</a>, <a href="https://publications.waset.org/abstracts/search?q=scientometrics" title=" scientometrics"> scientometrics</a> </p> <a href="https://publications.waset.org/abstracts/67802/analyzing-keyword-networks-for-the-identification-of-correlated-research-topics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67802.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">257</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31068</span> The Impact of Keyword and Full Video Captioning on Listening Comprehension</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elias%20Bensalem">Elias Bensalem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigates the effect of two types of captioning (full and keyword captioning) on listening comprehension. Thirty-six university-level EFL students participated in the study. They were randomly assigned to watch three video clips under three conditions. The first group watched the video clips with full captions. The second group watched the same video clips with keyword captions. The control group watched the video clips without captions. After watching each clip, participants took a listening comprehension test. At the end of the experiment, participants completed a questionnaire to measure their perceptions about the use of captions and the video clips they watched. Results indicated that the full captioning group significantly outperformed both the keyword captioning and the no captioning group on the listening comprehension tests. However, this study did not find any significant difference between the keyword captioning group and the no captioning group. Results of the survey suggest that keyword captioning were a source of distraction for participants. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=captions" title="captions">captions</a>, <a href="https://publications.waset.org/abstracts/search?q=EFL" title=" EFL"> EFL</a>, <a href="https://publications.waset.org/abstracts/search?q=listening%20comprehension" title=" listening comprehension"> listening comprehension</a>, <a href="https://publications.waset.org/abstracts/search?q=video" title=" video"> video</a> </p> <a href="https://publications.waset.org/abstracts/62467/the-impact-of-keyword-and-full-video-captioning-on-listening-comprehension" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62467.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">262</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31067</span> Empirical Study on Factors Influencing SEO</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pakinee%20Aimmanee">Pakinee Aimmanee</a>, <a href="https://publications.waset.org/abstracts/search?q=Phoom%20Chokratsamesiri"> Phoom Chokratsamesiri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Search engine has become an essential tool nowadays for people to search for their needed information on the internet. In this work, we evaluate the performance of the search engine from three factors: the keyword frequency, the number of inbound links, and the difficulty of the keyword. The evaluations are based on the ranking position and the number of days that Google has seen or detect the webpage. We find that the keyword frequency and the difficulty of the keyword do not affect the Google ranking where the number of inbound links gives remarkable improvement of the ranking position. The optimal number of inbound links found in the experiment is 10. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SEO" title="SEO">SEO</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title=" information retrieval"> information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20search" title=" web search"> web search</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20technologies" title=" knowledge technologies"> knowledge technologies</a> </p> <a href="https://publications.waset.org/abstracts/9414/empirical-study-on-factors-influencing-seo" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9414.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">283</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31066</span> Sentence vs. Keyword Content Analysis in Intellectual Capital Disclosures Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Martin%20Surya%20Mulyadi">Martin Surya Mulyadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yunita%20Anwar"> Yunita Anwar</a>, <a href="https://publications.waset.org/abstracts/search?q=Rosinta%20Ria%20Panggabean"> Rosinta Ria Panggabean</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Major transformations in economic activity from an agricultural economy to knowledge economy have led to an increasing focus on intellectual capital (IC) that has been characterized by continuous innovation, the spread of digital and communication technologies, intangible and human factors. IC is defined as the possession of knowledge and experience, professional knowledge and skill, proper relationships and technological capacities, which when applied will give organizations a competitive advantage. All of IC report/disclosure could be captured from the corporate annual report as it is a communication device that allows a corporation to connect with various external and internal stakeholders. This study was conducted using sentence-content analysis of IC disclosure in the annual report. This research aims to analyze whether the keyword-content analysis is reliable research methodology for IC disclosure related research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intellectual%20capital" title="intellectual capital">intellectual capital</a>, <a href="https://publications.waset.org/abstracts/search?q=intellectual%20capital%20disclosure" title=" intellectual capital disclosure"> intellectual capital disclosure</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20analysis" title=" content analysis"> content analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=annual%20report" title=" annual report"> annual report</a>, <a href="https://publications.waset.org/abstracts/search?q=sentence%20analysis" title=" sentence analysis"> sentence analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword%20analysis" title=" keyword analysis"> keyword analysis</a> </p> <a href="https://publications.waset.org/abstracts/66892/sentence-vs-keyword-content-analysis-in-intellectual-capital-disclosures-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66892.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">367</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31065</span> Emerging Research Trends in Routing Protocol for Wireless Sensor Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Subhra%20Prosun%20Paul">Subhra Prosun Paul</a>, <a href="https://publications.waset.org/abstracts/search?q=Shruti%20Aggarwal"> Shruti Aggarwal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Now a days Routing Protocol in Wireless Sensor Network has become a promising technique in the different fields of the latest computer technology. Routing in Wireless Sensor Network is a demanding task due to the different design issues of all sensor nodes. Network architecture, no of nodes, traffic of routing, the capacity of each sensor node, network consistency, service value are the important factor for the design and analysis of Routing Protocol in Wireless Sensor Network. Additionally, internal energy, the distance between nodes, the load of sensor nodes play a significant role in the efficient routing protocol. In this paper, our intention is to analyze the research trends in different routing protocols of Wireless Sensor Network in terms of different parameters. In order to explain the research trends on Routing Protocol in Wireless Sensor Network, different data related to this research topic are analyzed with the help of Web of Science and Scopus databases. The data analysis is performed from global perspective-taking different parameters like author, source, document, country, organization, keyword, year, and a number of the publication. Different types of experiments are also performed, which help us to evaluate the recent research tendency in the Routing Protocol of Wireless Sensor Network. In order to do this, we have used Web of Science and Scopus databases separately for data analysis. We have observed that there has been a tremendous development of research on this topic in the last few years as it has become a very popular topic day by day. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analysis" title="analysis">analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=routing%20protocol" title=" routing protocol"> routing protocol</a>, <a href="https://publications.waset.org/abstracts/search?q=research%20trends" title=" research trends"> research trends</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20network" title=" wireless sensor network"> wireless sensor network</a> </p> <a href="https://publications.waset.org/abstracts/141889/emerging-research-trends-in-routing-protocol-for-wireless-sensor-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141889.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">215</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31064</span> Modified Active (MA) Algorithm to Generate Semantic Web Related Clustered Hierarchy for Keyword Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20Leena%20Giri">G. Leena Giri</a>, <a href="https://publications.waset.org/abstracts/search?q=Archana%20Mathur"> Archana Mathur</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20H.%20Manjula"> S. H. Manjula</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20R.%20Venugopal"> K. R. Venugopal</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20M.%20Patnaik"> L. M. Patnaik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Keyword search in XML documents is based on the notion of lowest common ancestors in the labelled trees model of XML documents and has recently gained a lot of research interest in the database community. In this paper, we propose the Modified Active (MA) algorithm which is an improvement over the active clustering algorithm by taking into consideration the entity aspect of the nodes to find the level of the node pertaining to a particular keyword input by the user. A portion of the bibliography database is used to experimentally evaluate the modified active algorithm and results show that it performs better than the active algorithm. Our modification improves the response time of the system and thereby increases the efficiency of the system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=keyword%20matching%20patterns" title="keyword matching patterns">keyword matching patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=MA%20algorithm" title=" MA algorithm"> MA algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20search" title=" semantic search"> semantic search</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a> </p> <a href="https://publications.waset.org/abstracts/6608/modified-active-ma-algorithm-to-generate-semantic-web-related-clustered-hierarchy-for-keyword-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6608.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">413</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31063</span> Effect of the Keyword Strategy on Lexical Semantic Acquisition: Recognition, Retention and Comprehension in an English as Second Language Context</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatima%20Muhammad%20Shitu">Fatima Muhammad Shitu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study seeks to investigate the effect of the keyword strategy on lexico–semantic acquisition, recognition, retention and comprehension in an ESL context. The aim of the study is to determine whether the keyword strategy can be used to enhance acquisition. As a quasi- experimental research, the objectives of the study include: To determine the extent to which the scores obtained by the subjects, who were trained on the use of the keyword strategy for acquisition, differ at the pre-tests and the post–tests and also to find out the relationship in the scores obtained at these tests levels. The sample for the study consists of 300 hundred undergraduate ESL Students in the Federal College of Education, Kano. The seventy-five lexical items for acquisition belong to the lexical field category known as register, and they include Medical, Agriculture and Photography registers (MAP). These were divided in the ratio twenty-five (25) lexical items in each lexical field. The testing technique was used to collect the data while the descriptive and inferential statistics were employed for data analysis. For the purpose of testing, the two kinds of tests administered at each test level include the WARRT (Word Acquisition, Recognition, and Retention Test) and the CCPT (Cloze Comprehension Passage Test). The results of the study revealed that there are significant differences in the scores obtained between the pre-tests, and the post–tests and there are no correlations in the scores obtained as well. This implies that the keyword strategy has effectively enhanced the acquisition of the lexical items studied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=keyword" title="keyword">keyword</a>, <a href="https://publications.waset.org/abstracts/search?q=lexical" title=" lexical"> lexical</a>, <a href="https://publications.waset.org/abstracts/search?q=semantics" title=" semantics"> semantics</a>, <a href="https://publications.waset.org/abstracts/search?q=strategy" title=" strategy"> strategy</a> </p> <a href="https://publications.waset.org/abstracts/50708/effect-of-the-keyword-strategy-on-lexical-semantic-acquisition-recognition-retention-and-comprehension-in-an-english-as-second-language-context" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50708.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">311</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31062</span> Graph-Based Semantical Extractive Text Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mina%20Samizadeh">Mina Samizadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the past few decades, there has been an explosion in the amount of available data produced from various sources with different topics. The availability of this enormous data necessitates us to adopt effective computational tools to explore the data. This leads to an intense growing interest in the research community to develop computational methods focused on processing this text data. A line of study focused on condensing the text so that we are able to get a higher level of understanding in a shorter time. The two important tasks to do this are keyword extraction and text summarization. In keyword extraction, we are interested in finding the key important words from a text. This makes us familiar with the general topic of a text. In text summarization, we are interested in producing a short-length text which includes important information about the document. The TextRank algorithm, an unsupervised learning method that is an extension of the PageRank (algorithm which is the base algorithm of Google search engine for searching pages and ranking them), has shown its efficacy in large-scale text mining, especially for text summarization and keyword extraction. This algorithm can automatically extract the important parts of a text (keywords or sentences) and declare them as a result. However, this algorithm neglects the semantic similarity between the different parts. In this work, we improved the results of the TextRank algorithm by incorporating the semantic similarity between parts of the text. Aside from keyword extraction and text summarization, we develop a topic clustering algorithm based on our framework, which can be used individually or as a part of generating the summary to overcome coverage problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=keyword%20extraction" title="keyword extraction">keyword extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=n-gram%20extraction" title=" n-gram extraction"> n-gram extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20summarization" title=" text summarization"> text summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20clustering" title=" topic clustering"> topic clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20analysis" title=" semantic analysis"> semantic analysis</a> </p> <a href="https://publications.waset.org/abstracts/160526/graph-based-semantical-extractive-text-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160526.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">71</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31061</span> Keyword Advertising: Still Need Construction in European Union; Perspective on Interflora vs. Marks and Spencer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammadbagher%20Asghariaghamashhadi">Mohammadbagher Asghariaghamashhadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Internet users normally are automatically linked to an advertisement sponsored by a bidder when Internet users enter any trademarked keyword on a search engine. This advertisement appears beside the search results. Through the process of keyword advertising, advertisers can connect with many Internet users and let them know about their goods and services. This concept has generated heated disagreements among legal scholars, trademark proprietors, advertisers, search engine owners, and consumers. Therefore, use of trademarks in keyword advertising has been one of the most debatable issues in trademark law for several years. This entirely new way of using trademarks over the Internet has provoked a discussion concerning the core concepts of trademark law. In respect to legal issues, European Union (EU) trademark law is mostly governed by the Trademark Directive and the Community Trademark Regulation. Article 5 of the directive and Article 9 of the trademark regulation determine the circumstances in which a trademark owner holds the right to prohibit a third party’s use of his/her registered sign. Harmonized EU trademark law proved to be ambiguous on whether using of a trademark is amounted to trademark infringement or not. The case law of the European Court of Justice (ECJ), with reference to this legislation, is mostly unfavorable to trademark owners. This ambivalence was also exhibited by the case law of EU Member States. European keyword advertisers simply could not tell which use of a competitor‘s trademark was lawful. In recent years, ECJ has continuously expanded the scope and reach of trademark protection in the EU. It is notable that Inconsistencies in the Court’s system of infringement criteria clearly come to the fore and this approach has been criticized by analysts who believe that the Court should have adopted a more traditional approach to the analysis of trademark infringement, which was suggested by its Advocate General, in order to arrive at the same conclusion. Regarding case law of keyword advertising within Europe, one of the most disputable cases is Interflora vs. Marks and Spencer, which is still on-going. This study examines and critically analyzes the decisions of the ECJ, the high court of England, and the Court of Appeals of England and address critically keyword advertising issue within European trademark legislation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ECJ" title="ECJ">ECJ</a>, <a href="https://publications.waset.org/abstracts/search?q=Google" title=" Google"> Google</a>, <a href="https://publications.waset.org/abstracts/search?q=Interflora" title=" Interflora"> Interflora</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword%20advertising" title=" keyword advertising"> keyword advertising</a>, <a href="https://publications.waset.org/abstracts/search?q=Marks%20and%20Spencer" title=" Marks and Spencer"> Marks and Spencer</a>, <a href="https://publications.waset.org/abstracts/search?q=trademark%20infringement" title=" trademark infringement"> trademark infringement</a> </p> <a href="https://publications.waset.org/abstracts/51502/keyword-advertising-still-need-construction-in-european-union-perspective-on-interflora-vs-marks-and-spencer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51502.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">345</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31060</span> Optimization Query Image Using Search Relevance Re-Ranking Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20G.%20Asmitha%20Chandini">T. G. Asmitha Chandini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Web-based image search re-ranking, as an successful method to get better the results. In a query keyword, the first stair is store the images is first retrieve based on the text-based information. The user to select a query keywordimage, by using this query keyword other images are re-ranked based on their visual properties with images.Now a day to day, people projected to match images in a semantic space which is used attributes or reference classes closely related to the basis of semantic image. though, understanding a worldwide visual semantic space to demonstrate highly different images from the web is difficult and inefficient. The re-ranking images, which automatically offline part learns dissimilar semantic spaces for different query keywords. The features of images are projected into their related semantic spaces to get particular images. At the online stage, images are re-ranked by compare their semantic signatures obtained the semantic précised by the query keyword image. The query-specific semantic signatures extensively improve both the proper and efficiency of image re-ranking. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Query" title="Query">Query</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword" title=" keyword"> keyword</a>, <a href="https://publications.waset.org/abstracts/search?q=image" title=" image"> image</a>, <a href="https://publications.waset.org/abstracts/search?q=re-ranking" title=" re-ranking"> re-ranking</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic" title=" semantic"> semantic</a>, <a href="https://publications.waset.org/abstracts/search?q=signature" title=" signature"> signature</a> </p> <a href="https://publications.waset.org/abstracts/28398/optimization-query-image-using-search-relevance-re-ranking-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28398.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">552</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31059</span> A Corpus-Based Analysis of "MeToo" Discourse in South Korea: Coverage Representation in Korean Newspapers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sun-Hee%20Lee">Sun-Hee Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Amanda%20Kraley"> Amanda Kraley</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The “MeToo” movement is a social movement against sexual abuse and harassment. Though the hashtag went viral in 2017 following different cultural flashpoints in different countries, the initial response was quiet in South Korea. This radically changed in January 2018, when a high-ranking senior prosecutor, Seo Ji-hyun, gave a televised interview discussing being sexually assaulted by a colleague. Acknowledging public anger, particularly among women, on the long-existing problems of sexual harassment and abuse, the South Korean media have focused on several high-profile cases. Analyzing the media representation of these cases is a window into the evolving South Korean discourse around “MeToo.” This study presents a linguistic analysis of “MeToo” discourse in South Korea by utilizing a corpus-based approach. The term corpus (pl. corpora) is used to refer to electronic language data, that is, any collection of recorded instances of spoken or written language. A “MeToo” corpus has been collected by extracting newspaper articles containing the keyword “MeToo” from BIGKinds, big data analysis, and service and Nexis Uni, an online academic database search engine, to conduct this language analysis. The corpus analysis explores how Korean media represent accusers and the accused, victims and perpetrators. The extracted data includes 5,885 articles from four broadsheet newspapers (Chosun, JoongAng, Hangyore, and Kyunghyang) and 88 articles from two Korea-based English newspapers (Korea Times and Korea Herald) between January 2017 and November 2020. The information includes basic data analysis with respect to keyword frequency and network analysis and adds refined examinations of select corpus samples through naming strategies, semantic relations, and pragmatic properties. Along with the exponential increase of the number of articles containing the keyword “MeToo” from 104 articles in 2017 to 3,546 articles in 2018, the network and keyword analysis highlights ‘US,’ ‘Harvey Weinstein’, and ‘Hollywood,’ as keywords for 2017, with articles in 2018 highlighting ‘Seo Ji-Hyun, ‘politics,’ ‘President Moon,’ ‘An Ui-Jeong, ‘Lee Yoon-taek’ (the names of perpetrators), and ‘(Korean) society.’ This outcome demonstrates the shift of media focus from international affairs to domestic cases. Another crucial finding is that word ‘defamation’ is widely distributed in the “MeToo” corpus. This relates to the South Korean legal system, in which a person who defames another by publicly alleging information detrimental to their reputation—factual or fabricated—is punishable by law (Article 307 of the Criminal Act of Korea). If the defamation occurs on the internet, it is subject to aggravated punishment under the Act on Promotion of Information and Communications Network Utilization and Information Protection. These laws, in particular, have been used against accusers who have publicly come forward in the wake of “MeToo” in South Korea, adding an extra dimension of risk. This corpus analysis of “MeToo” newspaper articles contributes to the analysis of the media representation of the “MeToo” movement and sheds light on the shifting landscape of gender relations in the public sphere in South Korea. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corpus%20linguistics" title="corpus linguistics">corpus linguistics</a>, <a href="https://publications.waset.org/abstracts/search?q=MeToo" title=" MeToo"> MeToo</a>, <a href="https://publications.waset.org/abstracts/search?q=newspapers" title=" newspapers"> newspapers</a>, <a href="https://publications.waset.org/abstracts/search?q=South%20Korea" title=" South Korea"> South Korea</a> </p> <a href="https://publications.waset.org/abstracts/133017/a-corpus-based-analysis-of-metoo-discourse-in-south-korea-coverage-representation-in-korean-newspapers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133017.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">223</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31058</span> An Open Source Advertisement System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pushkar%20Umaranikar">Pushkar Umaranikar</a>, <a href="https://publications.waset.org/abstracts/search?q=Chris%20Pollett"> Chris Pollett</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An online advertisement system and its implementation for the Yioop open source search engine are presented. This system supports both selling advertisements and displaying them within search results. The selling of advertisements is done using a system to auction off daily impressions for keyword searches. This is an open, ascending price auction system in which all accepted bids will receive a fraction of the auctioned day’s impressions. New bids in our system are required to be at least one half of the sum of all previous bids ensuring the number of accepted bids is logarithmic in the total ad spend on a keyword for a day. The mechanics of creating an advertisement, attaching keywords to it, and adding it to an advertisement inventory are described. The algorithm used to go from accepted bids for a keyword to which ads are displayed at search time is also presented. We discuss properties of our system and compare it to existing auction systems and systems for selling online advertisements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=online%20markets" title="online markets">online markets</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20ad%20system" title=" online ad system"> online ad system</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20auctions" title=" online auctions"> online auctions</a>, <a href="https://publications.waset.org/abstracts/search?q=search%20engines" title=" search engines"> search engines</a> </p> <a href="https://publications.waset.org/abstracts/46605/an-open-source-advertisement-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46605.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">326</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31057</span> Climate Change and Tourism: A Scientometric Analysis Using Citespace</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yan%20Fang">Yan Fang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jie%20Yin"> Jie Yin</a>, <a href="https://publications.waset.org/abstracts/search?q=Bihu%20Wu"> Bihu Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The interaction between climate change and tourism is one of the most promising research areas of recent decades. In this paper, a scientometric analysis of 976 academic publications between 1990 and 2015 related to climate change and tourism is presented in order to characterize the intellectual landscape by identifying and visualizing the evolution of the collaboration network, the co-citation network, and emerging trends of citation burst and keyword co-occurrence. The results show that the number of publications in this field has increased rapidly and it has become an interdisciplinary and multidisciplinary topic. The research areas are dominated by Australia, USA, Canada, New Zealand, and European countries, which have the most productive authors and institutions. The hot topics of climate change and tourism research in recent years are further identified, including the consequences of climate change for tourism, necessary adaptations, the vulnerability of the tourism industry, tourist behaviour and demand in response to climate change, and emission reductions in the tourism sector. The work includes an in-depth analysis of a major forum of climate change and tourism to help readers to better understand global trends in this field in the past 25 years. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=climate%20change" title="climate change">climate change</a>, <a href="https://publications.waset.org/abstracts/search?q=tourism" title=" tourism"> tourism</a>, <a href="https://publications.waset.org/abstracts/search?q=scientometrics" title=" scientometrics"> scientometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=CiteSpace" title=" CiteSpace"> CiteSpace</a> </p> <a href="https://publications.waset.org/abstracts/59883/climate-change-and-tourism-a-scientometric-analysis-using-citespace" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59883.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">414</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31056</span> Identification of Spam Keywords Using Hierarchical Category in C2C E-Commerce</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shao%20Bo%20Cheng">Shao Bo Cheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Yong-Jin%20Han"> Yong-Jin Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Se%20Young%20Park"> Se Young Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Seong-Bae%20Park"> Seong-Bae Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Consumer-to-Consumer (C2C) E-commerce has been growing at a very high speed in recent years. Since identical or nearly-same kinds of products compete one another by relying on keyword search in C2C E-commerce, some sellers describe their products with spam keywords that are popular but are not related to their products. Though such products get more chances to be retrieved and selected by consumers than those without spam keywords, the spam keywords mislead the consumers and waste their time. This problem has been reported in many commercial services like e-bay and taobao, but there have been little research to solve this problem. As a solution to this problem, this paper proposes a method to classify whether keywords of a product are spam or not. The proposed method assumes that a keyword for a given product is more reliable if the keyword is observed commonly in specifications of products which are the same or the same kind as the given product. This is because that a hierarchical category of a product in general determined precisely by a seller of the product and so is the specification of the product. Since higher layers of the hierarchical category represent more general kinds of products, a reliable degree is differently determined according to the layers. Hence, reliable degrees from different layers of a hierarchical category become features for keywords and they are used together with features only from specifications for classification of the keywords. Support Vector Machines are adopted as a basic classifier using the features, since it is powerful, and widely used in many classification tasks. In the experiments, the proposed method is evaluated with a golden standard dataset from Yi-han-wang, a Chinese C2C e-commerce, and is compared with a baseline method that does not consider the hierarchical category. The experimental results show that the proposed method outperforms the baseline in F1-measure, which proves that spam keywords are effectively identified by a hierarchical category in C2C e-commerce. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spam%20keyword" title="spam keyword">spam keyword</a>, <a href="https://publications.waset.org/abstracts/search?q=e-commerce" title=" e-commerce"> e-commerce</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword%20features" title=" keyword features"> keyword features</a>, <a href="https://publications.waset.org/abstracts/search?q=spam%20%EF%AC%81ltering" title=" spam ﬁltering"> spam ﬁltering</a> </p> <a href="https://publications.waset.org/abstracts/15501/identification-of-spam-keywords-using-hierarchical-category-in-c2c-e-commerce" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15501.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">294</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31055</span> Finding Related Scientific Documents Using Formal Concept Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nadeem%20Akhtar">Nadeem Akhtar</a>, <a href="https://publications.waset.org/abstracts/search?q=Hira%20Javed"> Hira Javed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An important aspect of research is literature survey. Availability of a large amount of literature across different domains triggers the need for optimized systems which provide relevant literature to researchers. We propose a search system based on keywords for text documents. This experimental approach provides a hierarchical structure to the document corpus. The documents are labelled with keywords using KEA (Keyword Extraction Algorithm) and are automatically organized in a lattice structure using Formal Concept Analysis (FCA). This groups the semantically related documents together. The hierarchical structure, based on keywords gives out only those documents which precisely contain them. This approach open doors for multi-domain research. The documents across multiple domains which are indexed by similar keywords are grouped together. A hierarchical relationship between keywords is obtained. To signify the effectiveness of the approach, we have carried out the experiment and evaluation on Semeval-2010 Dataset. Results depict that the presented method is considerably successful in indexing of scientific papers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=formal%20concept%20analysis" title="formal concept analysis">formal concept analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword%20extraction%20algorithm" title=" keyword extraction algorithm"> keyword extraction algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=scientific%20documents" title=" scientific documents"> scientific documents</a>, <a href="https://publications.waset.org/abstracts/search?q=lattice" title=" lattice"> lattice</a> </p> <a href="https://publications.waset.org/abstracts/71401/finding-related-scientific-documents-using-formal-concept-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71401.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">332</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31054</span> A Social Network Analysis of the Palestinian Feminist Network Tal3at</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maath%20M.%20Musleh">Maath M. Musleh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research aims to study recent trends in the Palestinian feminist movement through the case study of Tal3at. The study uses social network analysis as its primary method to analyze Twitter data. It attempts to interpret results through the lens of network theories and Parson’s AGIL paradigm. The study reveals major structural weaknesses in the Tal3at network. Our findings suggest that the movement will decline soon as sentiments of alienation amongst Palestinian women increases. These findings were validated by a couple of central actors in the network. This study contributes an SNA approach to the understanding of the understudied Palestinian feminism. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feminism" title="feminism">feminism</a>, <a href="https://publications.waset.org/abstracts/search?q=Palestine" title=" Palestine"> Palestine</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20network%20analysis" title=" social network analysis"> social network analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Tal3at" title=" Tal3at"> Tal3at</a> </p> <a href="https://publications.waset.org/abstracts/136124/a-social-network-analysis-of-the-palestinian-feminist-network-tal3at" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136124.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">264</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31053</span> Modelling the Education Supply Chain with Network Data Envelopment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sourour%20Ramzi">Sourour Ramzi</a>, <a href="https://publications.waset.org/abstracts/search?q=Claudia%20Sarrico"> Claudia Sarrico</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Little has been done on network DEA in education, and nobody has attempted to model the whole education supply chain using network DEA. As such the contribution of the present paper is to propose a model for measuring the efficiency of education supply chains using network DEA. First, we use a general survey of data envelopment analysis (DEA) to establish the emergent themes for research in DEA, and focus on the theme of Network DEA. Second, we use a survey on two-stage DEA models, and Network DEA to write a state of the art on Network DEA, particularly applied to supply chain management. Third, we use a survey on DEA applications to establish the most influential papers on DEA education applications, in order to establish the state of the art on applications of DEA in education, in general, and applications of DEA to education using network DEA, in particular. Finally, we propose a model for measuring the performance of education supply chains of different education systems (countries or states within a country, for instance). We then use this model on some empirical data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=supply%20chain" title="supply chain">supply chain</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20envelopment%20analysis" title=" data envelopment analysis"> data envelopment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20DEA" title=" network DEA"> network DEA</a> </p> <a href="https://publications.waset.org/abstracts/57510/modelling-the-education-supply-chain-with-network-data-envelopment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57510.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">368</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31052</span> Interactive, Topic-Oriented Search Support by a Centroid-Based Text Categorisation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mario%20Kubek">Mario Kubek</a>, <a href="https://publications.waset.org/abstracts/search?q=Herwig%20Unger"> Herwig Unger</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Centroid terms are single words that semantically and topically characterise text documents and so may serve as their very compact representation in automatic text processing. In the present paper, centroids are used to measure the relevance of text documents with respect to a given search query. Thus, a new graphbased paradigm for searching texts in large corpora is proposed and evaluated against keyword-based methods. The first, promising experimental results demonstrate the usefulness of the centroid-based search procedure. It is shown that especially the routing of search queries in interactive and decentralised search systems can be greatly improved by applying this approach. A detailed discussion on further fields of its application completes this contribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=search%20algorithm" title="search algorithm">search algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=centroid" title=" centroid"> centroid</a>, <a href="https://publications.waset.org/abstracts/search?q=query" title=" query"> query</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword" title=" keyword"> keyword</a>, <a href="https://publications.waset.org/abstracts/search?q=co-occurrence" title=" co-occurrence"> co-occurrence</a>, <a href="https://publications.waset.org/abstracts/search?q=categorisation" title=" categorisation"> categorisation</a> </p> <a href="https://publications.waset.org/abstracts/82581/interactive-topic-oriented-search-support-by-a-centroid-based-text-categorisation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82581.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">282</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31051</span> Frontier Dynamic Tracking in the Field of Urban Plant and Habitat Research: Data Visualization and Analysis Based on Journal Literature</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shao%20Qi">Shao Qi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The article uses the CiteSpace knowledge graph analysis tool to sort and visualize the journal literature on urban plants and habitats in the Web of Science and China National Knowledge Infrastructure databases. Based on a comprehensive interpretation of the visualization results of various data sources and the description of the intrinsic relationship between high-frequency keywords using knowledge mapping, the research hotspots, processes and evolution trends in this field are analyzed. Relevant case studies are also conducted for the hotspot contents to explore the means of landscape intervention and synthesize the understanding of research theories. The results show that (1) from 1999 to 2022, the research direction of urban plants and habitats gradually changed from focusing on plant and animal extinction and biological invasion to the field of human urban habitat creation, ecological restoration, and ecosystem services. (2) The results of keyword emergence and keyword growth trend analysis show that habitat creation research has shown a rapid and stable growth trend since 2017, and ecological restoration has gained long-term sustained attention since 2004. The hotspots of future research on urban plants and habitats in China may focus on habitat creation and ecological restoration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=research%20trends" title="research trends">research trends</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20analysis" title=" visual analysis"> visual analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=habitat%20creation" title=" habitat creation"> habitat creation</a>, <a href="https://publications.waset.org/abstracts/search?q=ecological%20restoration" title=" ecological restoration"> ecological restoration</a> </p> <a href="https://publications.waset.org/abstracts/173295/frontier-dynamic-tracking-in-the-field-of-urban-plant-and-habitat-research-data-visualization-and-analysis-based-on-journal-literature" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173295.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">61</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31050</span> Impact Assessment of Information Communication, Network Providers, Teledensity, and Consumer Complaints on Gross Domestic Products</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Essang%20Anwana%20Onuntuei">Essang Anwana Onuntuei</a>, <a href="https://publications.waset.org/abstracts/search?q=Chinyere%20Blessing%20Azunwoke"> Chinyere Blessing Azunwoke</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study used secondary data from foreign and local organizations to explore major challenges and opportunities abound in Information Communication. The study aimed at exploring the tie between tele density (network coverage area) and the number of network subscriptions, probing if the degree of consumer complaints varies significantly among network providers, and assessing if network subscriptions do significantly influence the sector’s GDP contribution. Methods used for data analysis include Pearson product-moment correlation and regression analysis, and the Analysis of Variance (ANOVA) as well. At a two-tailed test of 0.05 confidence level, the results of findings established about 85.6% of network subscriptions were explained by tele density (network coverage area), and the number of network subscriptions; Consumer Complaints’ degree varied significantly among network providers as 80.158291 (F calculated) > 3.490295 (F critical) with very high confidence associated p-value = 0.000000 which is < 0.05; and finally, 65% of the nation’s GDP was explained by network subscription to show a high association. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tele%20density" title="tele density">tele density</a>, <a href="https://publications.waset.org/abstracts/search?q=subscription" title=" subscription"> subscription</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20coverage" title=" network coverage"> network coverage</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20communication" title=" information communication"> information communication</a>, <a href="https://publications.waset.org/abstracts/search?q=consumer" title=" consumer"> consumer</a> </p> <a href="https://publications.waset.org/abstracts/188468/impact-assessment-of-information-communication-network-providers-teledensity-and-consumer-complaints-on-gross-domestic-products" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/188468.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">45</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31049</span> A Network Approach to Analyzing Financial Markets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yusuf%20Seedat">Yusuf Seedat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The necessity to understand global financial markets has increased following the unfortunate spread of the recent financial crisis around the world. Financial markets are considered to be complex systems consisting of highly volatile move-ments whose indexes fluctuate without any clear pattern. Analytic methods of stock prices have been proposed in which financial markets are modeled using common network analysis tools and methods. It has been found that two key components of social network analysis are relevant to modeling financial markets, allowing us to forecast accurate predictions of stock prices within the financial market. Financial markets have a number of interacting components, leading to complex behavioral patterns. This paper describes a social network approach to analyzing financial markets as a viable approach to studying the way complex stock markets function. We also look at how social network analysis techniques and metrics are used to gauge an understanding of the evolution of financial markets as well as how community detection can be used to qualify and quantify in-fluence within a network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=network%20analysis" title="network analysis">network analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20networks" title=" social networks"> social networks</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20markets" title=" financial markets"> financial markets</a>, <a href="https://publications.waset.org/abstracts/search?q=stocks" title=" stocks"> stocks</a>, <a href="https://publications.waset.org/abstracts/search?q=nodes" title=" nodes"> nodes</a>, <a href="https://publications.waset.org/abstracts/search?q=edges" title=" edges"> edges</a>, <a href="https://publications.waset.org/abstracts/search?q=complex%20networks" title=" complex networks"> complex networks</a> </p> <a href="https://publications.waset.org/abstracts/142621/a-network-approach-to-analyzing-financial-markets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142621.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">191</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31048</span> Interbank Networks and the Benefits of Using Multilayer Structures </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Danielle%20Sandler%20dos%20Passos">Danielle Sandler dos Passos</a>, <a href="https://publications.waset.org/abstracts/search?q=Helder%20Coelho"> Helder Coelho</a>, <a href="https://publications.waset.org/abstracts/search?q=Fl%C3%A1via%20Mori%20Sarti"> Flávia Mori Sarti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Complexity science seeks the understanding of systems adopting diverse theories from various areas. Network analysis has been gaining space and credibility, namely with the biological, social and economic systems. Significant part of the literature focuses only monolayer representations of connections among agents considering one level of their relationships, and excludes other levels of interactions, leading to simplistic results in network analysis. Therefore, this work aims to demonstrate the advantages of the use of multilayer networks for the representation and analysis of networks. For this, we analyzed an interbank network, composed of 42 banks, comparing the centrality measures of the agents (degree and PageRank) resulting from each method (monolayer <em>x</em> multilayer). This proved to be the most reliable and efficient the multilayer analysis for the study of the current networks and highlighted JP Morgan and Deutsche Bank as the most important banks of the analyzed network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=complexity" title="complexity">complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=interbank%20networks" title=" interbank networks"> interbank networks</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20networks" title=" multilayer networks"> multilayer networks</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20analysis" title=" network analysis"> network analysis</a> </p> <a href="https://publications.waset.org/abstracts/92811/interbank-networks-and-the-benefits-of-using-multilayer-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92811.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">282</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31047</span> A Survey of Sentiment Analysis Based on Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pingping%20Lin">Pingping Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Xudong%20Luo"> Xudong Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Yifan%20Fan"> Yifan Fan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=document%20analysis" title="document analysis">document analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20sentiment%20analysis" title=" multimodal sentiment analysis"> multimodal sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a> </p> <a href="https://publications.waset.org/abstracts/130107/a-survey-of-sentiment-analysis-based-on-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130107.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">164</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31046</span> The Coauthorship Network Analysis of the Norwegian School of Economics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ivan%20Belik">Ivan Belik</a>, <a href="https://publications.waset.org/abstracts/search?q=Kurt%20Jornsten"> Kurt Jornsten</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We construct the coauthorship network based on the scientific collaboration between the faculty members at the Norwegian School of Economics (NHH) and based on their international academic publication experience. The network structure is based on the NHH faculties’ publications recognized by the ISI Web of Science for the period 1950 – Spring, 2014. The given network covers the publication activities of the NHH faculty members (over six departments) based on the information retrieved from the ISI Web of Science in Spring, 2014. In this paper we analyse the constructed coauthorship network in different aspects of the theory of social networks analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coauthorship%20networks" title="coauthorship networks">coauthorship networks</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20networks%20analysis" title=" social networks analysis"> social networks analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Norwegian%20School%20of%20Economics" title=" Norwegian School of Economics"> Norwegian School of Economics</a>, <a href="https://publications.waset.org/abstracts/search?q=ISI" title=" ISI"> ISI</a> </p> <a href="https://publications.waset.org/abstracts/15216/the-coauthorship-network-analysis-of-the-norwegian-school-of-economics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15216.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">432</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31045</span> The Relation Between Social Capital and Trust with Social Network Analysis (SNA)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Safak%20Baykal">Safak Baykal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study is analyzing the relationship between self leadership and social capital of people with using Social Network Analysis. In this study, two aspects of social capital will be focused: bonding, homophilous social capital (BoSC) which implies better, strong, dense or closed network ties, and bridging, heterophilous social capital (BrSC) which implies weak ties, bridging the structural holes. The other concept of the study is Trust (Tr), namely interpersonal trust, willingness to ascribe good intentions to and have confidence in the words and actions of other people. In this study, the sample group, 61 people, was selected from a private firm from the defense industry. The relation between BoSC/BrSC and Tr is shown by using Social Network Analysis (SNA) and statistical analysis with Likert type-questionnaire. The results of the analysis show the Cronbach’s alpha value is 0.73 and social capital values (BoSC/BrSC) is highly correlated with Tr values of the people. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bonding%20social%20capital" title="bonding social capital">bonding social capital</a>, <a href="https://publications.waset.org/abstracts/search?q=bridging%20social%20capital" title=" bridging social capital"> bridging social capital</a>, <a href="https://publications.waset.org/abstracts/search?q=trust" title=" trust"> trust</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20network%20analysis%20%28SNA%29" title=" social network analysis (SNA)"> social network analysis (SNA)</a> </p> <a href="https://publications.waset.org/abstracts/35306/the-relation-between-social-capital-and-trust-with-social-network-analysis-sna" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35306.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">529</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31044</span> Evidence of a Negativity Bias in the Keywords of Scientific Papers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kseniia%20Zviagintseva">Kseniia Zviagintseva</a>, <a href="https://publications.waset.org/abstracts/search?q=Brett%20Buttliere"> Brett Buttliere</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Science is fundamentally a problem-solving enterprise, and scientists pay more attention to the negative things, that cause them dissonance and negative affective state of uncertainty or contradiction. While this is agreed upon by philosophers of science, there are few empirical demonstrations. Here we examine the keywords from those papers published by PLoS in 2014 and show with several sentiment analyzers that negative keywords are studied more than positive keywords. Our dataset is the 927,406 keywords of 32,870 scientific articles in all fields published in 2014 by the journal PLOS ONE (collected from Altmetric.com). Counting how often the 47,415 unique keywords are used, we can examine whether those negative topics are studied more than positive. In order to find the sentiment of the keywords, we utilized two sentiment analysis tools, Hu and Liu (2004) and SentiStrength (2014). The results below are for Hu and Liu as these are the less convincing results. The average keyword was utilized 19.56 times, with half of the keywords being utilized only 1 time and the maximum number of uses being 18,589 times. The keywords identified as negative were utilized 37.39 times, on average, with the positive keywords being utilized 14.72 times and the neutral keywords - 19.29, on average. This difference is only marginally significant, with an F value of 2.82, with a p of .05, but one must keep in mind that more than half of the keywords are utilized only 1 time, artificially increasing the variance and driving the effect size down. To examine more closely, we looked at those top 25 most utilized keywords that have a sentiment. Among the top 25, there are only two positive words, ‘care’ and ‘dynamics’, in position numbers 5 and 13 respectively, with all the rest being identified as negative. ‘Diseases’ is the most studied keyword with 8,790 uses, with ‘cancer’ and ‘infectious’ being the second and fourth most utilized sentiment-laden keywords. The sentiment analysis is not perfect though, as the words ‘diseases’ and ‘disease’ are split by taking 1st and 3rd positions. Combining them, they remain as the most common sentiment-laden keyword, being utilized 13,236 times. More than just splitting the words, the sentiment analyzer logs ‘regression’ and ‘rat’ as negative, and these should probably be considered false positives. Despite these potential problems, the effect is apparent, as even the positive keywords like ‘care’ could or should be considered negative, since this word is most commonly utilized as a part of ‘health care’, ‘critical care’ or ‘quality of care’ and generally associated with how to improve it. All in all, the results suggest that negative concepts are studied more, also providing support for the notion that science is most generally a problem-solving enterprise. The results also provide evidence that negativity and contradiction are related to greater productivity and positive outcomes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bibliometrics" title="bibliometrics">bibliometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=keywords%20analysis" title=" keywords analysis"> keywords analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=negativity%20bias" title=" negativity bias"> negativity bias</a>, <a href="https://publications.waset.org/abstracts/search?q=positive%20and%20negative%20words" title=" positive and negative words"> positive and negative words</a>, <a href="https://publications.waset.org/abstracts/search?q=scientific%20papers" title=" scientific papers"> scientific papers</a>, <a href="https://publications.waset.org/abstracts/search?q=scientometrics" title=" scientometrics"> scientometrics</a> </p> <a href="https://publications.waset.org/abstracts/76958/evidence-of-a-negativity-bias-in-the-keywords-of-scientific-papers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76958.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">186</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31043</span> Analysis of Scholarly Communication Patterns in Korean Studies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Erin%20Hea-Jin%20Kim">Erin Hea-Jin Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to investigate scholarly communication patterns in Korean studies, which focuses on all aspects of Korea, including history, culture, literature, politics, society, economics, religion, and so on. It is called ‘national study or home study’ as the subject of the study is itself, whereas it is called ‘area study’ as the subject of the study is others, i.e., outside of Korea. Understanding of the structure of scholarly communication in Korean studies is important since the motivations, procedures, results, or outcomes of individual studies may be affected by the cooperative relationships that appear in the communication structure. To this end, we collected 1,798 articles with the (author or index) keyword ‘Korean’ published in 2018 from the Scopus database and extracted the institution and country of the authors using a text mining technique. A total of 96 countries, including South Korea, was identified. Then we constructed a co-authorship network based on the countries identified. The indicators of social network analysis (SNA), co-occurrences, and cluster analysis were used to measure the activity and connectivity of participation in collaboration in Korean studies. As a result, the highest frequency of collaboration appears in the following order: S. Korea with the United States (603), S. Korea with Japan (146), S. Korea with China (131), S. Korea with the United Kingdom (83), and China with the United States (65). This means that the most active participants are S. Korea as well as the USA. The highest rank in the role of mediator measured by betweenness centrality appears in the following order: United States (0.165), United Kingdom (0.045), China (0.043), Japan (0.037), Australia (0.026), and South Africa (0.023). These results show that these countries contribute to connecting in Korean studies. We found two major communities among the co-authorship network. Asian countries and America belong to the same community, and the United Kingdom and European countries belong to the other community. Korean studies have a long history, and the study has emerged since Japanese colonization. However, Korean studies have never been investigated by digital content analysis. The contributions of this study are an analysis of co-authorship in Korean studies with a global perspective based on digital content, which has not attempted so far to our knowledge, and to suggest ideas on how to analyze the humanities disciplines such as history, literature, or Korean studies by text mining. The limitation of this study is that the scholarly data we collected did not cover all domestic journals because we only gathered scholarly data from Scopus. There are thousands of domestic journals not indexed in Scopus that we can consider in terms of national studies, but are not possible to collect. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=co-authorship%20network" title="co-authorship network">co-authorship network</a>, <a href="https://publications.waset.org/abstracts/search?q=Korean%20studies" title=" Korean studies"> Korean studies</a>, <a href="https://publications.waset.org/abstracts/search?q=Koreanology" title=" Koreanology"> Koreanology</a>, <a href="https://publications.waset.org/abstracts/search?q=scholarly%20communication" title=" scholarly communication"> scholarly communication</a> </p> <a href="https://publications.waset.org/abstracts/116651/analysis-of-scholarly-communication-patterns-in-korean-studies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116651.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">158</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31042</span> Using Mixed Methods in Studying Classroom Social Network Dynamics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nashrawan%20Naser%20Taha">Nashrawan Naser Taha</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrew%20M.%20Cox"> Andrew M. Cox</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In a multi-cultural learning context, where ties are weak and dynamic, combining qualitative with quantitative research methods may be more effective. Such a combination may also allow us to answer different types of question, such as about people’s perception of the network. In this study the use of observation, interviews and photos were explored as ways of enhancing data from social network questionnaires. Integrating all of these methods was found to enhance the quality of data collected and its accuracy, also providing a richer story of the network dynamics and the factors that shaped these changes over time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mixed%20methods" title="mixed methods">mixed methods</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20network%20analysis" title=" social network analysis"> social network analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-cultural%20learning" title=" multi-cultural learning"> multi-cultural learning</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20network%20dynamics" title=" social network dynamics"> social network dynamics</a> </p> <a href="https://publications.waset.org/abstracts/15500/using-mixed-methods-in-studying-classroom-social-network-dynamics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15500.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">510</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31041</span> Methodologies for Deriving Semantic Technical Information Using an Unstructured Patent Text Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaehyung%20An">Jaehyung An</a>, <a href="https://publications.waset.org/abstracts/search?q=Sungjoo%20Lee"> Sungjoo Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Patent documents constitute an up-to-date and reliable source of knowledge for reflecting technological advance, so patent analysis has been widely used for identification of technological trends and formulation of technology strategies. But, identifying technological information from patent data entails some limitations such as, high cost, complexity, and inconsistency because it rely on the expert’ knowledge. To overcome these limitations, researchers have applied to a quantitative analysis based on the keyword technique. By using this method, you can include a technological implication, particularly patent documents, or extract a keyword that indicates the important contents. However, it only uses the simple-counting method by keyword frequency, so it cannot take into account the sematic relationship with the keywords and sematic information such as, how the technologies are used in their technology area and how the technologies affect the other technologies. To automatically analyze unstructured technological information in patents to extract the semantic information, it should be transformed into an abstracted form that includes the technological key concepts. Specific sentence structure ‘SAO’ (subject, action, object) is newly emerged by representing ‘key concepts’ and can be extracted by NLP (Natural language processor). An SAO structure can be organized in a problem-solution format if the action-object (AO) states that the problem and subject (S) form the solution. In this paper, we propose the new methodology that can extract the SAO structure through technical elements extracting rules. Although sentence structures in the patents text have a unique format, prior studies have depended on general NLP (Natural language processor) applied to the common documents such as newspaper, research paper, and twitter mentions, so it cannot take into account the specific sentence structure types of the patent documents. To overcome this limitation, we identified a unique form of the patent sentences and defined the SAO structures in the patents text data. There are four types of technical elements that consist of technology adoption purpose, application area, tool for technology, and technical components. These four types of sentence structures from patents have their own specific word structure by location or sequence of the part of speech at each sentence. Finally, we developed algorithms for extracting SAOs and this result offer insight for the technology innovation process by providing different perspectives of technology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=NLP" title="NLP">NLP</a>, <a href="https://publications.waset.org/abstracts/search?q=patent%20analysis" title=" patent analysis"> patent analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=SAO" title=" SAO"> SAO</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic-analysis" title=" semantic-analysis"> semantic-analysis</a> </p> <a href="https://publications.waset.org/abstracts/43480/methodologies-for-deriving-semantic-technical-information-using-an-unstructured-patent-text-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43480.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 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